Background Characteristics of  Postsecondary Institutions In fall 2015, almost 20 million students were enrolled in over 4,500 2- and  4-year postsecondary institutions, according to IPEDS data.  Postsecondary institutions vary in terms of their funding, the length and  type of programs offered, and instructional mission, among other  characteristics. Public institutions, which include state universities and  community colleges, are traditionally supported by federal, state, and  local funds, in addition to revenue from tuition and fees. Private, not-for- profit schools are owned and operated by independent or religious  organizations, and their net earnings do not benefit any shareholder or  individual. Tuition and fees as well as other revenue sources primarily  support these schools. For-profit institutions are privately owned and  earnings can benefit shareholders or individuals. Two-year institutions  often provide career-oriented programs at the certificate and associate’s  degree levels. Four-year institutions tend to have a broad range of  instructional programs at the undergraduate level leading to bachelor’s  degrees. Many 4-year institutions also offer master’s or doctorate level  programs, and some 4-year institutions have a research focus. The landscape of postsecondary institutions has changed over the past  20 years, particularly with respect to for-profit institutions. The number of  public institutions remained relatively constant and the number of private  institutions declined slightly; however, the number of for-profit institutions  more than tripled between 1995 and 2011 before declining slightly to  2015 levels (see fig. 1). How National Data Count  Faculty IPEDS and CPS both provide data on postsecondary faculty. IPEDS data can provide information on positions filled by different types  of faculty across postsecondary education or by types of institutions (see  sidebar for how we categorize institutions using IPEDS data). In terms  of faculty types, IPEDS distinguishes between tenure-track and  contingent positions and also has data on graduate assistants, though we  cannot determine whether these graduate teaching assistants are the  instructors of record for courses or are instead providing classroom  support (e.g., grading, leading discussions, and lab setup). Because  IPEDS counts positions, any faculty who teach at more than one  institution are counted multiple times—for each position they fill. CPS counts the number of actual workers in a given occupation and, in  terms of faculty, provides data on how many individuals are employed as  postsecondary teachers in colleges and universities nationwide. CPS  does not differentiate faculty by type of institution or by tenure status. For  example, CPS cannot identify full-time contingent faculty separately from  full-time tenure-track faculty. Contingent Faculty  Fill Most Instructional  Positions Nationwide  and Teach Close to  Half or More of All  Courses at Public  Institutions in Three  Selected States From 1995 to 2011, the  Number of Instructional  Positions Filled by  Contingent Faculty More  than Doubled While Those  Filled by Full-Time Tenure- track Faculty Increased By  10 Percent According to IPEDS data, from 1995 to 2011, the percentage of  postsecondary instructional positions filled by contingent faculty increased  from 57.6 to 71.6 percent. During this period the number of instructional  faculty positions at all institutions nationwide grew by over 60 percent— though most of this growth was among positions held by contingent  faculty. More specifically, the number of positions held by full-time and  part-time non-tenure-track faculty—which we define as contingent—both  doubled during this period, while the number of positions held by full-time  tenure-track faculty grew by about 10 percent (see table 1). In addition to  full- and part-time contingent faculty, some graduate assistants may also  teach courses. During the same period, the number of graduate teaching  assistant positions grew by 63.8 percent. Some of the increase in the percentage of contingent faculty positions is  due to the growth of the for-profit sector and growth among 2-year  institutions, which as a whole rely primarily on contingent faculty. For  example, the number of positions nationwide across for-profit institutions  in 2011 was almost 9 times as many as in 1995. However, the shift  towards contingent faculty positions was clear even among only 4-year  public and private institutions (see fig. 2). Contingent Faculty Fill  about 70 Percent of  Instructional Positions  Nationwide, Though This  Varies Greatly by  Institution and Many of  These Positions Have  Some Job Stability Contingent faculty currently fill most instructional positions nationwide,  though these numbers cannot be compared to historical data. According  to 2015 IPEDS data, contingent faculty fill 69.5 percent of the 1,444,774  postsecondary instructional positions across all institutions nationwide,  including about 61.4 percent of instructional positions at 4-year  institutions, 83.5 percent at 2-year institutions, and 99.7 percent at for- profit institutions (see fig. 3). As noted previously, aggregated IPEDS  data count faculty who teach at multiple institutions multiple times;  therefore, there are likely more contingent faculty positions than there are  contingent faculty workers. Although it is unknown how many faculty hold  jobs at multiple institutions, this is likely to be more prevalent among  faculty filling part-time positions. To illustrate, according to CPS data— which counts individuals—an estimated 31.7 percent (+/- 4.1) of  individuals employed as postsecondary teachers in colleges and  universities worked part-time in 2015. In contrast, according to IPEDS  data, part-time faculty held about 50.0 percent of instructional positions. Though the majority of instructional faculty positions across institutions  are contingent, employment stability among these positions may vary  widely. Many of these contingent positions may have some job stability,  depending on contract specifics. For example, about a quarter of  contingent positions across all institutions have full-time, annual, multi- year, or potentially pseudo-tenure contracts (see fig. 3). Some of these  positions may expire at the end of a set term or have no option for  renewal—potentially requiring a new application process—while others  may be relatively long-term with continuously repeating contracts. For  example, officials at one North Dakota institution we visited described  their non-tenure-track positions as “tenure light” because full-time faculty  receive 1-year contracts for their first 4 years and then, after a successful  promotion review, receive continuous 3-year contracts that can be  terminated only for adequate cause, such as gross professional  misconduct. In contrast to these more stable contingent positions, more  than half of the contingent positions across all institutions nationwide are  part-time and have less-than-annual contracts or lack faculty status— which we define as being among the least stable (see fig. 3). For some  of the faculty filling these positions, this employment may be their sole  source of income. Similar to contingent workers in the broader labor  force, as we reported previously, these faculty may face volatility and  uncertainty in their economic circumstances. Other faculty in these  positions may have employment or sources of income outside of  teaching. For example, some part-time instructors are employed full-time  in their fields and teach on the side as subject-matter experts or to stay  connected with their local university community. Examples of Part-Time Faculty Situations from Faculty Discussion Groups at  Selected Institutions  Two part-time faculty members at an institution in Ohio said they had jobs outside of  teaching and said they teach on the side because they love it, rather than relying on  it for subsistence. One part-time faculty member at an institution in Georgia said that she was retired,  but teaches courses to keep a foot in the education world while also enjoying free  time in retirement. One younger part-time faculty member at an institution in North Dakota stated that  she teaches on a semester-to-semester contract and that this was her primary  employment. While it is unknown how many faculty rely on their instructional positions  as their primary employment, nationally representative data from the  Current Population Survey (CPS) and Survey of Doctorate Recipients  (SDR) provide some limited information that suggests many part-time  faculty prefer working part-time. The CPS data show that an estimated  46.2 percent (+/- 6.3) of part-time faculty reported wanting to work part- time, while only 10.0 percent (+/- 5.1) reported working part-time because  they could only find a part-time job or because of seasonal or temporary  fluctuations in the availability of employment. Similarly, SDR data on  doctorate-holding instructional faculty in STEM (science, technology,  engineering, and math), health, and social sciences fields show that most  part-time contingent faculty report wanting to work part-time, though  among those who reported wanting a full-time job, most reported not  being able to find one (see table 2). According to IPEDS data, different types of postsecondary institutions rely  more heavily on different segments of the instructional workforce. As  shown in figure 4, many 4-year institutions employ tenure-track, full-time  contingent, and part-time contingent positions—though the balance  varies. Far fewer 2-year institutions and very few for-profit institutions  have tenure-track positions. Part-time and short-term positions are  substantially more prevalent at these institutions. For example, part-time  contingent positions make up 67.9 percent and 80.5 percent of  instructional positions at 2-year and for-profit institutions, respectively, as  compared to 39.8 percent at 4-year institutions. Beyond institution type, reliance on different types of faculty positions also  varies by institutional characteristics, such as size and highest degree  offered. For example, across 4-year institutions with more than 10,000  students, 43.1 percent of positions are tenure-track, as compared to 30.6  percent across institutions with fewer than 5,000 students. Similarly, a  higher percentage of instructional positions are tenure-track across 4-year  institutions that offer doctorate degrees, compared to those institutions  that do not offer doctorate degrees (see fig. 5). At 4-Year Public  Institutions in Three  Selected States,  Contingent Faculty Teach  Close to Half or More of All  Courses and Credit Hours Contingent faculty fill more than half of instructional positions at 2- or 4- year public institutions in the three selected states (see fig. 6). Two-year  public institutions in North Dakota and Ohio were especially reliant on  contingent faculty, where they fill about 72 and 84 percent of instructional  positions, respectively (see sidebar for our definition of instructional  faculty in the state data, as compared to our other data analyses). We examined several different demographic characteristics of contingent  faculty including gender, race, educational attainment, and age. Gender According to 2015 IPEDS data, instructional positions nationwide are  divided roughly evenly between the sexes, but women fill fewer tenure- track positions and more contingent positions than men do. As shown in  figure 7, across all institutions, women hold a substantially lower  proportion of full-time tenured positions (38.4 percent) than men do,  though women fill 48.9 percent of full-time positions that are on a tenure  track but not yet tenured, and that are generally more recent hires. Across  all institutions, women also hold a slightly greater proportion of contingent  positions (about 53 percent). This imbalance in representation, in part,  reflects the higher concentration of women at 2-year and for-profit  institutions, where they fill 54.3 and 55.9 percent of positions,  respectively. These institutions generally rely more heavily on contingent  faculty positions than do 4-year institutions. White (non-Hispanic) faculty fill almost three-quarters of instructional  positions across all institutions nationwide. This racial/ethnic  representation is relatively consistent across full-time tenure-track, full- time contingent, and part-time positions. Though filling 27.6 percent of  positions across all institutions, racial and ethnic minorities have slightly  greater representation at institutions in large cities (33.2 percent) and at  for-profit institutions (38.4 percent). Educational Attainment Our analysis of state data suggests that across 4-year public institutions  in North Dakota and Ohio, lower proportions of individuals in contingent  positions have a graduate or doctoral degree (see fig. 8). While the  differences between tenure-track and contingent faculty are substantial,  possible explanations include variation in degree requirements by  discipline or individual circumstances, such as having professional  experience in the field. Across public institutions in all three selected states, and excluding  positions held by instructional graduate students, most positions held by  the youngest faculty are contingent, and the most common positions held  by the oldest faculty are part-time contingent. More specifically, most  positions held by individuals under age 40 are contingent—60.2 percent  in Georgia, 66.9 percent in North Dakota, and 74.5 percent in Ohio  (excluding instructional graduate assistants). This suggests that newer  graduates may be more likely to be hired into contingent rather than  tenure-track positions. In addition, the most common positions held by  faculty ages 70 and older are part-time contingent positions—51.0  percent in Georgia, 45.5 percent in North Dakota, and 59.4 percent in  Ohio (excluding instructional graduate assistants). This suggests that a  segment of the part-time contingent workforce may consist of retirees or  workers who are approaching retirement. Administrators Said  Contingent Faculty  Have a Range of  Responsibilities, and  They Consider  Multiple Needs When  Determining Faculty  Makeup Full-Time Contingent  Faculty at Institutions We  Visited May Have a Variety  of Responsibilities, but  Part-Time Contingent  Faculty Generally Focus  on Teaching According to administrators we interviewed, institutions utilize full-time  contingent faculty for different purposes, which may involve  responsibilities beyond teaching. Administrators said full-time contingent  faculty are hired primarily to teach and generally have larger course loads  than tenure-track faculty who may teach fewer courses per semester due  to significant research responsibilities. However, they also noted that— similar to tenure-track faculty—many full-time contingent faculty carry out  additional responsibilities. For example, some full-time contingent faculty  may perform service, conduct research, advise students, serve as  department chairs, or manage student recruitment efforts for their  programs. Many other full-time contingent faculty serve as instructors or  lecturers whose sole responsibility is to teach. For example,  administrators from one institution explained that they employ  professional instructors who teach four courses per semester and have  no service or research responsibilities. In addition, some full-time  contingent faculty are hired because they have certain professional  qualifications or experience. For example, one institution we visited  employed academic professionals who may teach one or two courses per  year while carrying out administrative, marketing, mentoring, or other  duties. While full-time contingent faculty may have a variety of responsibilities,  administrators stated that part-time contingent faculty generally focus on  teaching, though they also may fulfill different purposes. In some cases,  part-time contingent faculty serve as expert practitioners who teach  specific subject matter. For example, administrators from one institution  said that they hire part-time contingent faculty to teach instrumental music  courses because teaching each instrument requires specialized  expertise, and there may not be enough students learning any single  instrument to warrant a full-time position. In other cases, part-time  contingent faculty teach general education courses, such as Introduction  to English Composition, which most students are required to take. In  addition, while some part-time contingent faculty may have full-time jobs  outside of academia, others may be working toward long-term careers as  tenure-track professors, according to administrators. Administrators from  some institutions also told us that they hire part-time contingent faculty  help to manage lab courses (e.g., setting up laboratory equipment,  assisting students) or to serve as mentors to students in specific  programs (e.g., theological studies). Administrators Consider  Financial, Institutional,  Faculty, and Student  Needs When Determining  Faculty Makeup University and college administrators we interviewed identified a number  of financial and institutional considerations as well as faculty and student  needs that affect their decisions regarding faculty makeup (see fig. 9). Administrators stated that utilizing contingent faculty allows for flexibility in  managing various financial considerations, including the following: Budget uncertainty: Administrators from several public institutions  explained that utilizing contingent faculty helps them manage  uncertainty regarding the level of public funding they may receive.  Administrators have the option not to renew contracts of contingent  faculty if they experience a decrease in their funding, whereas  institutions commit to retain tenure-track faculty until they retire. In  addition, administrators from several public institutions noted that, as  a result of decreased state funding, they have become more reliant on  tuition to meet their budget needs. They told us that hiring contingent  faculty to focus on teaching rather than research allows the institution  to offer more classes and serve additional students, which in turn,  generates more tuition revenue. Compensation costs: Administrators stated that, in general, they  cannot employ tenure-track faculty for all courses because they can  be more expensive to employ than contingent faculty. In addition to  the long-term commitment associated with tenure, other costs may  include spending to support research conducted by tenure-track  faculty (e.g., investment in specialized labs or equipment). Legal or grant program requirements: Some administrators said that  legal or grant program requirements affect their decisions regarding  the utilization of contingent faculty. For example, administrators from  several institutions told us that they had reduced teaching loads for  part-time faculty because the Patient Protection and Affordable Care  Act (PPACA) requires certain employers to provide health insurance  for employees working 30 hours or more per week. Administrators  from another institution stated that they utilized in-house faculty and  hired additional contingent faculty to staff a federal grant program  aimed at providing training for inmates at correctional facilities  because—after receiving notification that they had been awarded the  grant—they had approximately 2 months to staff 160 course  sections. In addition, since they did not know whether the grant  would be renewed, they did not know whether they would be able to  retain those faculty at the end of the program. Institutional Considerations Administrators said that utilizing contingent faculty also allows flexibility to  meet different institutional needs. Examples of institutional considerations  cited by administrators include the following: Enrollment: By utilizing contingent faculty, institutions have more  flexibility to meet course demand if there is a surge in enrollment or to  downsize if there is a drop in enrollment, according to administrators.  For example, administrators from one 2-year institution noted that  enrollment generally increases when the economy is weak and  decreases when the economy is strong. These administrators also  said that their enrollment fluctuates greatly with changes in the  economy and that, in their experience, prospective students are more  likely to choose 4-year institutions rather than 2-year institutions when  the economy is strong. In addition, when offering a course,  administrators said part-time faculty may teach that course during a  trial period while administrators decide whether to offer the course  long term. Location and market demand: Some administrators stated that they  offer contingent faculty positions in response to market conditions. For  example, administrators from institutions located in small towns or  rural areas said they rely on local professionals to teach certain  courses on a part-time basis, in part, because of challenges finding  qualified faculty and having fewer students enrolled at remote sites.  Some administrators also said contingent faculty positions offer  certain advantages that help them recruit high quality instructors. For  example, administrators from one university noted that their institution  offers stable, full-time employment to recent graduates looking to gain  experience before applying for tenure-track positions at other  institutions. Specialized experience: Contingent faculty may bring professional  expertise to certain courses. For example, administrators from several  institutions stated that their programs for health professionals rely on  contingent faculty working in their field to teach clinical courses so that  students may gain experience at an established medical practice.  Administrators said that hiring practitioners from local industry as part- time instructors is an effective way to support specialized courses that  have a limited number of sections. Administrators from one institution  also noted that practitioners may have the qualifications needed to  meet accreditation requirements for certain programs and  departments (e.g., professional and technical programs). Balancing priorities: Administrators said that utilizing a combination of  tenure-track and contingent faculty helps their institutions fulfill both  teaching and research missions and accommodate the hiring needs of  different programs and departments. For example, administrators  from one institution noted that the additional revenue from increased  course offerings—staffed by part-time contingent faculty—allows them  to invest more money in research programs for tenure-track faculty.  Administrators from two institutions explained that hiring part-time  contingent faculty in a given department allows them to reallocate  resources as needed, for example, to hire full-time contingent or  tenure-track positions in another department. In addition, while  contingent faculty may help fulfill accreditation requirements for  certain programs, administrators from several institutions also stated  that their accrediting bodies require a balance of contingent and  tenure-track faculty, or alternatively, full-time and part-time contingent  faculty. For example, administrators from one 4-year institution told us  that part-time faculty may teach no more than 25 percent of student  credit hours within their business school. Faculty Needs As part of faculty utilization decisions, administrators said that they  consider the personal and professional needs of faculty. Examples of  faculty needs cited by administrators include the following: Flexibility: Administrators told us that they offer part-time positions, in  part, because many qualified candidates want to work part-time for  professional, family, or other reasons. For example, administrators  at one institution said that part-time contingent faculty positions allow  expert-practitioners to continue working full-time in their field while  pursuing an interest in teaching. Alternatively, for those teaching as  full-time contingent faculty, in some cases, their position may offer a  more predictable schedule or other benefits compared to their  professional field. Course loads: Administrators at some institutions said they prioritize  the professional needs of existing full-time faculty before hiring part- time faculty by ensuring that full-time faculty have enough courses to  meet their required teaching loads. Career paths: Some institutions have established mechanisms to  support long-term career paths for full-time contingent faculty. For  example, administrators from one institution stated that full-time  contingent faculty may qualify for multi-year contracts that can be  terminated only for adequate cause, such as gross professional  misconduct. Administrators from several institutions said that they  offer the full set of professorial ranks (i.e., Assistant Professor,  Associate Professor, and Professor) to some full-time contingent  faculty positions in order to provide opportunities for advancement. Student Needs Administrators stated that having a combination of tenure-track and  contingent faculty—or full-time and part-time contingent faculty at  institutions without tenure—is necessary to meet different student needs.  Examples of student needs cited by administrators include the following: Learning opportunities: Administrators stated that different types of  faculty may offer different opportunities to students. For example,  administrators told us that tenure-track faculty may provide research  and academic networking opportunities whereas contingent faculty  may not have the same opportunities to develop professional  networks or conduct research in their field. Some administrators  also said that the academic freedom associated with tenure or having  faculty who conduct research in their field may be beneficial to  students. Nonetheless, administrators from several institutions  emphasized that contingent faculty were equally qualified to teach and  that their positions allowed them to focus on teaching. Administrators  also noted that contingent faculty may bring professional expertise  and real-world experiences to the classroom. In addition to courses  that require specialized experience, administrators from one institution  said they also value the outside experience that contingent faculty  bring to general education courses. As an example, they stated that  part-time contingent faculty with experience from other jobs or  professions may be able to relate to the real-world needs of their  students because the majority of students will seek employment  outside of academia. Community: Administrators said that, regardless of tenure status, they  depend on having full-time faculty to help create a sense of  community. They discussed informal ways that faculty support their  campus community. For example, some administrators noted that full- time faculty contribute by mentoring students and participating in  activities on campus. In contrast, part-time faculty are not able to  spend as much time on campus because they often have other jobs  or commitments, according to administrators. Absent National  Information on Pay  Rates, Contingent  Faculty in Two  Selected States Are  Paid Less per  Course, and  Relatively Few Part- Time Faculty Receive  Health or Retirement  Benefits  Data from Two States  Show Contingent Faculty  Are Paid Less per Course,  Though Disparities Shrink  If Pay for Research and  Service Is Excluded National data on contingent faculty pay rates are not available, but data  from two states show that contingent faculty are paid less per course.  IPEDS data cannot be used to determine faculty pay rates because  salary data are not collected for part-time faculty nor are they collected at  the individual faculty level, and CPS data do not differentiate between full- time tenure-track and full-time contingent faculty. Given the limitations of  national data, we used data from two states to compare annual earnings  across different types of faculty. The differences in median annual  earnings shown in table 5 provide some insight into the generally lower  overall compensation of contingent faculty, though these data are not  generalizable. Further, particularly for part-time faculty who may be paid  on a piecemeal or per-course basis, this measure does not provide  information about whether compensation differences are due to lower pay  rates or less work performed (e.g., courses taught or hours worked). Thus, we use the state data to calculate and examine comparable pay  rates per course for all faculty types. Private organizations have  attempted to collect data specifically on pay-per-course rates for part-time  faculty, though efforts have been limited. On a per-course basis, we found that contingent faculty at public  institutions in two states are paid less per course taught, on average,  than full-time tenure-track faculty, though the extent of differences varies  depending on contingent faculty group and pay measure. We  conducted regression analyses of total pay per course and instructional  pay per course, which provide two different perspectives on faculty  compensation (see sidebar for explanations of these approaches and  see appendix I for details on our methods). These analyses controlled for  other factors that may affect earnings, such as employing institution,  discipline, highest degree earned, and demographics. As shown in  table 6, in terms of total pay per course, we found the following: Part-time contingent faculty in both states are paid about 75 percent  less per course regardless of whether the population includes all  faculty or is limited to “primarily teaching” faculty. The primarily  teaching group excludes faculty who primarily hold other roles  unrelated to instruction (e.g., administrators and research faculty). Full-time contingent faculty are paid about 35 percent less per course  in North Dakota and about 40 percent less per course in Ohio, among  primarily teaching faculty—differences are larger in Ohio if all faculty  are included. Instructional graduate assistants earn more per course than part-time  faculty (though still less than full-time tenure-track faculty). However,  compensation for these groups is fundamentally different because  instructional graduate assistants generally receive a stipend, similar to  an annual salary, rather than being paid by the course like many part- time faculty. In addition, graduate assistantships may be awarded for  academic merit or recruitment, and could also be considered as  compensation for a graduate assistant’s work as a student. Disparities in instructional pay per course—which measures pay for  equivalent work (see sidebar above)—are smaller for all contingent  faculty groups than those for total pay per course. As shown in table 7,  we found the following: Part-time contingent faculty in both states are paid about 60 percent  less per course regardless of whether the population includes all  faculty or is limited to primarily teaching faculty. Among primarily teaching faculty in both states, full-time contingent  faculty are paid about 10 percent less per course than full-time tenure- track faculty. As with total pay, the instructional pay disparity for full-time contingent  faculty in Ohio is larger if all faculty are included. However, when all  faculty are included in North Dakota, the pay difference between full- time contingent and full-time tenure-track faculty is not significant at  the 95 percent confidence level. Consistent with our other findings, when we analyzed national data from  the 2013 Survey of Doctorate Recipients (SDR), we also found that  contingent faculty in sciences fields earned less annually than full-time  tenure-track faculty. Full-time contingent faculty earned 22 percent less  than full-time tenure-track faculty, on average, and part-time contingent  faculty earned 70 percent less, among instructional, doctorate-holding  faculty in STEM, health, and social sciences fields. Unlike our analyses  of state data, the SDR analysis cannot account for differences in the  number of courses taught, and thus the results represent the combined  effects of lower pay rates and smaller workloads, to the extent either  exists. Relatively Few Part-Time  Contingent Faculty  Receive Health or  Retirement Benefits from  Their Employment Data from North Dakota and Georgia, as well as national data covering  different populations, suggest that relatively few part-time contingent  faculty receive health or retirement benefits from their employment though  full-time contingent faculty may. Although not generalizable, data from  North Dakota and Georgia include data on actual benefits provided to  faculty by institutions, as opposed to self-reported rates of coverage  found in national survey data. Relatively few part-time contingent faculty  and instructional graduate assistants in the North Dakota and Georgia  data receive retirement, health, and life insurance benefits from their  employment. For example, in Georgia and North Dakota, about 98  percent or more of individuals in full-time tenure-track and full-time  contingent positions receive work-provided retirement benefits, compared  to 19.4 and 9.3 percent, respectively, of those in part-time contingent  positions (see table 8). An even smaller percentage of instructional  graduate assistants in both states receive any of these benefits from their  employment; however, instructional graduate assistants are students, so  the terms of their employment may be different than traditional full-time  and part-time employees. Similarly, our analysis of SDR and CPS data show that relatively few part- time contingent faculty nationwide receive retirement benefits from their  employment. According to the 2013 SDR data, among instructional,  doctorate-holding faculty in STEM, health, and social sciences fields, an  estimated 48.4 percent (+/- 4.2) of part-time contingent faculty report  having access to “a retirement plan to which  employer contributed,”  compared to the vast majority of full-time tenure-track and full-time  contingent faculty. According to CPS data covering employment in  2015, an estimated 16.6 percent (+/- 6.1) of part-time faculty report  participating in a work-provided retirement plan, as compared to 60.8  percent (+/- 4.7) of full-time faculty. National Data on Health  Insurance Benefits While comparing health insurance coverage is complicated because  workers may be covered by other family members’ plans, in both the SDR  and CPS data, smaller proportions of part-time faculty had health  insurance through their own employment. According to the 2013 SDR  data, only 39.4 percent (+/- 4.6) of part-time contingent faculty had access  to “health insurance that was at least partially paid by  employer”  compared to almost all full-time tenure-track and full-time contingent  faculty. Similarly, in the CPS data, much smaller percentages of part- time faculty than full-time faculty report having health insurance through  their own employment (see table 9). Data from a 2013 Sample  of Faculty with Doctorates  Show That Contingent  Faculty Were Less  Satisfied with Certain  Aspects of their Economic  Circumstances In addition to the lower pay and access to benefits experienced by some  contingent faculty, among a national sample of instructional, doctorate- holding faculty in STEM, health, and social sciences fields, contingent  faculty were less satisfied with their job security and career prospects.  Based on our analysis of 2013 SDR data, the vast majority of all  instructional faculty, including contingent faculty, stated that they are very  or somewhat satisfied with their employment overall. However, compared  to full-time tenure-track faculty, more contingent faculty reported some  level of dissatisfaction (see fig. 10). While most faculty reported  satisfaction with their employment, at least a third of both full- and part- time contingent faculty stated that they are dissatisfied with their job  security and opportunities for career advancement. For example, an  estimated 55.1 percent (+/- 4.5) of part-time contingent faculty reported  some level of dissatisfaction with opportunities for advancement (see fig.  10), and the proportion who said they were very dissatisfied—26.1  percent (+/- 3.8)—is around 5 times greater than for full-time tenure-track  faculty. While Contingent  Faculty at Selected  Institutions Said Their  Work Offers Certain  Advantages, They  Expressed Concerns  about Contracts,  Wages, and  Institutional Support Contingent Faculty  Identified Certain  Advantages of Their Work Contingent faculty at selected institutions said their work offers certain  advantages, including those allowing them to balance professional and  personal responsibilities, develop skills, or work with students. Part-time  contingent faculty in some discussion groups said they choose to work  part-time because it gives them needed flexibility to balance teaching with  working full-time or to meet family needs, such as childcare or caring for  sick parents. As stated previously, our analysis of nationally  representative 2013 SDR data showed that, among a sample of  instructional faculty with doctorate degrees in STEM, health, and social  sciences fields, many faculty preferred to work part-time for reasons  including family responsibilities or holding another job. In terms of  developing skills, one instructional graduate assistant told us that having  teaching experience gives her an advantage in the job market. In  addition, in both full- and part-time discussion groups, some contingent  faculty told us they primarily want to teach, and their roles allow them to  do that rather than having to conduct research or take on other  responsibilities. In some discussion groups, contingent faculty said they  are committed to teaching because they find it rewarding to interact with  students. Insight from a Full-Time Contingent Faculty Member about Connecting with  Students  “I have yet to meet a contingent faculty member that does not say that student contact is  extremely important to them…We’re excellent teachers. We’re interested in teaching. We  are interested in being with students.” Contingent Faculty  Expressed Concerns  about Short-term  Contracts, Untimely  Contract Renewals, and  Compensation Contract-Related Concerns Contingent faculty in some of our discussion groups expressed concerns  about contractual issues. In particular, they cited concerns regarding  contract length, untimely contract renewals, or insufficient notice about  their class schedules. Full- and part-time contingent faculty said short- term contracts—annual or semester-to-semester contracts—produce  anxiety about job stability because of uncertainty about whether contracts  will be renewed. Part-time faculty who teach at multiple institutions  additionally said that short-term contracts hinder their ability to form  lasting relationships with institutions or students. In some discussion  groups, full- and part-time contingent faculty said untimely contract  renewals can make it difficult to find another position if a contract is not  renewed. For example, a full-time contingent faculty member said she  received notification in August that her contract was not being renewed  for the fall semester, at which point she could not find another position  elsewhere for that semester. Part-time contingent faculty told us that  notices about the status of their class schedules are also sometimes  untimely. One full-time contingent faculty member said that, when he  worked part-time, he sometimes did not know, until the first night of class,  that a course he was scheduled to teach had been given to a full-time  faculty member instead. While some contingent faculty expressed  concerns about contract lengths and renewals, some contingent faculty  said they do not have concerns in this area. Faculty members in some  part-time discussion groups told us teaching is not their primary source of  income or they are retired, so they are not concerned about job security  and contract renewals. Insight from a Full-Time Contingent Faculty Member  “The lack of long term job security/stability that results from short term contracts is my  biggest concern. I find it insulting when comments like “great work, we’re committed to  you” are coupled with actions like one year contracts when I have been in this position for  15 years. It does not make me feel valued.” Compensation-Related  Concerns Contingent faculty we spoke with identified insufficient compensation as a  disadvantage of their employment (see table 10). Full-time and part-time  contingent faculty in some discussion groups said they must supplement  their teaching income to cover their living expenses. For example, one  full-time contingent faculty member said he does consulting work,  bookkeeping, and product reviews to increase his income because his  teaching salary is not adequate. In addition, some part-time faculty said  they teach at several institutions to make ends meet financially and some  instructional graduate assistants also said they take on extra work to  cover living expenses. Union officials at the national level said their  members have expressed similar concerns. Specifically, Service  Employees International Union (SEIU) officials told us some contingent  faculty members qualify for public assistance due to the low level of  compensation they receive. Insight from Part-Time Contingent Faculty Member Teaching at Multiple  Institutions  “Society at large, I think, associates the college professor with a rather well paid and  stable career. And I think most of us who worked in this field know that is anything but the  case.” Some contingent faculty in both full- and part-time discussion groups said  they are not paid for all of their job requirements or are  undercompensated given their qualifications. Full- and part-time  contingent faculty and graduate student instructors said they are required  to assume extra responsibilities at no additional pay. For example, a  faculty member in a full-time discussion group told us she was given  additional duties of advising 15 students and attending meetings, neither  of which was included in her contract. Both full- and part-time faculty in  some discussion groups said their pay is not commensurate with their  academic credentials. One full-time faculty member told us an  administrator with a doctorate who works in the local school district near  her institution is paid double her salary. Similarly, a part-time faculty  member told us her salary is less than $20 an hour, a rate she considers  as too low for a professional with a doctorate. Some Contingent Faculty  at Selected Institutions  Said They Have Limited  Career Advancement or  Institutional Involvement  Opportunities and Lack  Certain Types of  Professional Support  Limited Career Advancement  Opportunities Contingent faculty in some discussion groups said they would like to  move into a tenure-track or full-time position, but face barriers doing so,  and union officials expressed similar views. For example, one full-time  contingent faculty member told us teaching 6 to 10 classes per year does  not allow her time to conduct the research needed to be competitive for a  tenure-track position. In some discussion groups, both full- and part-time  faculty said that they perceive that their colleagues sometimes view them  as less capable because they are not tenure-track faculty. As a result,  these faculty may not be considered for tenure-track positions when they  become available. A part-time contingent faculty member who teaches at  multiple institutions noted that availability of full-time positions may be  limited because many institutions hire only part-time faculty. Union  officials from the American Association of University Professors (AAUP)  and SEIU also cited the decline in the availability of tenure-track positions  as a barrier regarding career advancement for contingent faculty. Insight from a Part-Time Contingent Faculty Member Who Teaches at Multiple  Institutions  “It wasn’t that long ago that once you went to work for a college as an adjunct and you  were there a certain number of years, there was a real expectation that you would be  offered a full-time position or at least you would move to an annual contract so you only  had to worry once a year. That’s disappearing. More and more colleges are moving away  from that. Also, a lot of colleges are moving away from full-time positions.” Limited Institutional  Involvement Contingent faculty in some discussion groups expressed concerns that  they do not have a voice in institutional decision-making because they  cannot serve on some department or university-level committees or vote  on particular issues. They explained that sometimes a school’s policy  prohibits their service or relevant policy is not clearly articulated. For  example, a full-time contingent faculty member told us that contingent  faculty members at her institution cannot participate on governance  committees, which she said leaves administrators free to ignore the  concerns of contingent faculty. Insight from a Full-Time Contingent Faculty Member  “We have no voice. We have no say. We have no governance. We don’t have any of that.  And yet, we all—every one of us around here earned the same degree, worked the same  amount. So there is huge inequality between choosing to focus on research primarily,  and therefore, getting this basic job guarantee until  die and choosing to focus on  teaching,  not having that , even though in many other ways we are  equivalent.” Contingent faculty in some discussion groups also told us they are  reluctant to voice their views because they do not have job protections.  For example, a full-time contingent faculty member in one discussion  group told us she would feel more comfortable speaking up if she had a  continuing contract rather than her current annual contract. An official  from the National Center for the Study of Collective Bargaining in Higher  Education and the Professions said that an issue for contingent faculty  broadly is whether they are protected by due process. He said it can be  unclear for contingent faculty whether they can be terminated without due  process consideration when, for example, a student complains about the  content of a faculty member’s lecture. Despite concerns about opportunities for institutional involvement,  contingent faculty told us they preferred to use informal mechanisms to  raise issues with the administration and had mixed views about the value  of unions. Several full- and part-time faculty members said they are  comfortable approaching their department chairperson or even university  administrators to ask questions or express concerns. In terms of unions,  some faculty in both full-time and part-time discussion groups said they  were opposed to unions based on prior experiences or not wanting to pay  dues. In contrast, some faculty said they thought a union could be  beneficial by helping with certain issues, such as compensation and  working conditions. Union officials told us there has been greater interest  in recent years from contingent faculty—including graduate assistants—in  learning about faculty unionization or in organizing into unions. However,  one union official noted that it can be challenging for part-time faculty to  form a union because they may move from one institution to another. Institutional Support Examples of Academic Associations’  Efforts to Focus on Contingent Worker  Issues  The American Political Science Association  (APSA): Convened a committee in 2016 on  the status of contingent faculty in the  profession to expand ways to support  contingent faculty members. The committee  sponsored a roundtable at the APSA Annual  Meeting in August 2017 to examine a range of  topics related to contingent faculty, including  promotion paths, fairness within the  profession, and the role of unionization.  The American Sociological Association (ASA):  Formed a task force on contingent faculty in  November 2015 to examine the implications  of the recent growth of contingent  employment among sociologists. The task  force’s interim report, issued in August 2017,  includes recommendations to ASA and  universities, for improving contingent faculty  working conditions.  The Modern Language Association: (MLA)  Convened a committee that will work through  June 2019 to examine issues that affect  contingent faculty, including salary and  benefits, workplace issues and conditions of  employment, demographics, participation in  departmental and institutional governance,  academic freedom, and professional  development. The committee plans to identify  effective policies and practices related to  contingent faculty.  The American Institute of Physics (AIP):  Conducted a survey of individual faculty in  2016 that included questions on school  climate and culture. As of February 2017, AIP  was in the early stages of analyzing the  survey response rates and results. Contingent faculty in some discussion groups also described a lack of  institutional support in areas that can affect faculty teaching duties, such  as access to information systems or office space. For example, a part- time faculty member told us her access to institutional email and the  online grading system was terminated too soon because her contract  ended a few days before she gave final examinations. Part-time faculty  and faculty teaching at multiple institutions also raised concerns that they  sometimes lack appropriate office space to ensure student privacy. Union  officials we spoke with also said contingent faculty nationwide commonly  cite these areas of limited institutional support as concerns. Some  discipline-specific academic associations have also begun to focus on  issues related to contingent faculty (see sidebar). Insight from a Part-Time Contingent Faculty Member Who Teaches at Multiple  Institutions  “The office space problem is a big problem. Either one doesn’t have any office space or  it’s a jointly shared office space, a very large space with lots of people in it. It is very  difficult to have kind of close conversations with students. I think it brings up some Family  Educational Rights and Privacy Act (FERPA) problems, anonymity problems as well.” Agency Comments,  Third Party Views,  and Our Evaluation We provided a draft of this report to Education, NSF, and experts on  contingent faculty issues or the data used in this report for their review  and comment. Education did not have any comments. NSF and expert  reviewers provided technical comments, which we incorporated, as  appropriate. As agreed with your offices, unless you publicly announce the contents of  this report earlier, we plan no further distribution until 30 days from the  report date. At that time, we will send copies to the appropriate  congressional committees, to the Secretary of Education and the Director  of the National Science Foundation, and to other interested parties. In  addition, the report will be available at no charge on the GAO website at  http://www.gao.gov. If you or your staff have any questions about this report, please contact  me at (202) 512-7215 or brownbarnesc@gao.gov. Contact points for our  Offices of Congressional Relations and Public Affairs may be found on  the last page of this report. GAO staff who made key contributions to this  report are listed in appendix III. Appendix I: Objectives, Scope, and  Methodology The objectives of this review were to determine (1) what is known about  the makeup and utilization of the postsecondary instructional workforce;  (2) the roles different types of faculty fill at selected institutions and the  factors administrators consider when determining their faculty makeup;  (3) what is known about how economic circumstances compare across  different faculty types; and (4) what contingent faculty members report as  advantages and disadvantages of their work. To address objectives 2 and 4, we interviewed administrators and  contingent faculty members during site visits at selected institutions in  three states—Georgia, North Dakota, and Ohio. In each state, we visited  one 4-year public institution, one 4-year private (non-profit) institution, and  one 2-year public institution (see table 11). We selected institutions in  these states, in part, to provide context for our analysis of faculty and  course data that we obtained from their postsecondary data systems (see  Section 1 of this appendix for more information). In addition to data  availability, we considered size and geographic location as part of our  state selection process. When selecting institutions within each state, we  considered factors such as the size of the instructional faculty workforce,  the percentage of contingent faculty, and whether the institution is located  in an urban, suburban, or rural area. In our interviews with administrators—chief academic officers, vice  presidents, or deans, among others—we asked about the roles different  types of instructional faculty fill and the factors administrators consider  when determining their institution’s faculty makeup. In addition to  administrators at the institutions above, we also interviewed  administrators from one large online-based for-profit institution, which we  selected primarily based on size of the institution. In total, we interviewed  administrators from 10 institutions. The findings from these interviews are  not generalizable. At each institution, we held discussion groups with full-time and part-time  contingent faculty and graduate student instructors, where applicable.  University administrators solicited participants for the discussion groups  on our behalf. During these discussion groups, we asked contingent  faculty broad, open-ended questions about the advantages and  disadvantages of their work and about their working conditions.  Participants were invited to complete a written questionnaire to provide  demographic information about themselves. Among the 109 contingent  faculty members who completed our questionnaire, the average age of  full- and part-time contingent faculty we met with was 53. Graduate  student instructors were younger, with an average age of 30. Contingent  faculty we interviewed came from a range of disciplines, including  English, music, engineering, and the health professions. The vast majority  of full- and part-time contingent faculty indicated that they held a master’s  or doctorate degree. At the institutions we visited in Georgia, North  Dakota, and Ohio, the majority of part-time faculty worked at one  institution. To ensure we collected a broad range of perspectives, we  conducted two additional discussion groups with contingent faculty who  taught at multiple institutions. In total, we conducted 21 discussion  groups with contingent faculty. Finally, we conducted additional interviews to obtain background and  context for our work. We met with individuals knowledgeable about issues  related to postsecondary faculty and unions representing postsecondary  faculty, including the American Association of University Professors and  the Service Employees International Union. For all questions, we also  reviewed relevant federal laws and regulations. The remainder of this appendix provides detailed information about the  data and quantitative analysis methods we used in our review, as follows: Section 1: Key data sources Section 2: Quantitative analysis methods used to address the  makeup, utilization, and economic circumstances of postsecondary  instructional faculty (objectives 1 and 3) Section 3: Pay-per-course regression analysis methods (objective 3) Section 4: Annual earnings regression analysis methods (objective 3) Section 1: Data  Sources To address our objectives, we used data from multiple sources (see table  12). To gain an understanding of and provide context for the relevant faculty  data that we analyzed, we interviewed officials from federal, state, and  non-governmental agencies who collect and maintain the respective  datasets, including the Department of Education (Education), Labor,  National Science Foundation, North Dakota University System (NDUS),  Ohio Department of Higher Education (ODHE), University System of  Georgia (USG), and American Academy of Arts & Sciences (AAAS). The Integrated Postsecondary Education Data System (IPEDS) and the  state administrative data represent the entire populations they cover, and  while the Current Population Survey (CPS), the Survey of Doctorate  Recipients (SDR), and the Humanities Departmental Survey (HDS) are  sample survey data, when weighted, they also represent the populations  they cover. Because the sample surveys followed a probability procedure  based on random selections, each respective sample is only one of a  large number of samples that might have been drawn. Since each sample  could have provided different estimates, we express our confidence in the  precision of our particular sample’s results as the margin of error (i.e. the  half width of the 95 percent confidence interval—for example, +/- 7  percentage points). This is the interval that would contain the actual  population value for 95 percent of the samples that could have been  drawn. Throughout our analyses, for estimates from survey data we  reported the applicable margins of error. In some cases, the confidence  intervals around our estimates were asymmetrical; however, we  presented the maximum half-width for simplicity and for a consistent and  conservative representation of the sampling error associated with our  estimates. Our analyses of CPS and SDR survey data are weighted  analyses using sample design information, replicate weights, and survey  analysis software to get the proper sample survey estimates and margins  of error. Additional details about the datasets follow. Integrated Postsecondary  Education Data System  (IPEDS) IPEDS is a system of interrelated surveys conducted annually by  Education’s National Center for Education Statistics (NCES). IPEDS  gathers information from every college, university, and technical and  vocational institution that participates in federal student financial aid  programs, as well as other institutions that report data voluntarily. In  2015, more than 7,500 institutions reported data to IPEDS. IPEDS  collects data in the following 12 areas: institutional characteristics;  completions; 12-month enrollment; fall enrollment; graduation rates;  200% graduation rates; student financial aid; outcome measures;  admissions; human resources; finance; and academic libraries. As of the  2005 IPEDS data collection, information on faculty and staff are collected  as part of the human resources survey component, and include  information on faculty demographics and types of positions, among other  things. We used IPEDS data from 1995, 1999, 2003, 2007, 2011, and  2015. We utilized IPEDS as our primary data source because we are  able to identify a universe of postsecondary institutions and also because  the data allow us to distinguish between tenure-track and contingent  positions. Current Population Survey  (CPS) Annual Social and  Economic Supplement  (ASEC) The CPS is sponsored jointly by the Census Bureau and the Department  of Labor’s Bureau of Labor Statistics. It is the source of official  government statistics on employment and unemployment in the United  States. The basic monthly survey is used to collect information on  employment, such as employment status, occupation, and industry, as  well as demographic information, among other things. The survey is  based on a sample of the civilian, non-institutionalized population of the  United States. Using a multistage stratified sample design, about 54,000  households are interviewed monthly based on area of residence to  represent the country as a whole and individual states; the total sample  also includes additional households that are not interviewed for various  reasons, such as not being reachable. In addition to these interviewed  and non-interviewed households from the basic CPS monthly sample, the  ASEC includes additional households; the total sample size for the 2016  ASEC was almost 100,000 households. The ASEC provides  supplemental data on work experience, income components, such as  earnings from employment, and noncash benefits, such as health  insurance coverage, among other things. Data on employment and  income refer to the preceding calendar year, although demographic data  refer to the time of the survey. This report used data from the March 2016  ASEC, which refers to employment and income during calendar year  2015. Survey of Doctorate  Recipients (SDR) SDR is a biennial survey conducted by the National Science Foundation’s  (NSF) National Center for Science and Engineering Statistics (NCSES)  that provides demographic and career history information about  individuals with a research doctoral degree in a science, technology,  engineering, and math (STEM), health, or social sciences field from a  U.S. academic institution. The survey follows a large sample of  individuals throughout their careers from the year they received their  doctoral degree until age 75, plus a sample of new doctoral recipients  added in each cycle. The survey includes questions regarding occupation  (including discipline area for postsecondary faculty), earnings, job  satisfaction, faculty tenure status, and faculty rank, among other topics.  While some data from the survey are released publicly, other data are  restricted from public use—including data on tenure and rank— in order  to protect the anonymity of survey respondents. This report used data  from the 2013 SDR, which refers to employment in February 2013. We  obtained the publicly available data and a few additional restricted-use  variables that NCSES recoded for our use. Faculty and Course Data  Received from Selected  States The data from Georgia, North Dakota, and Ohio contained variables on  faculty characteristics, earnings and benefits, and courses taught. We  developed data requests through discussions with officials in each state. Georgia Postsecondary  Institution Administrative Data  (USG data) The data from USG covered all 4-year public institutions in Georgia  identified in our IPEDS universe and included course and enrollment data  from an academic database merged with faculty and earnings data from  USG’s Human Resources Data Mart. The Georgia data also included  information on the percentage of individual faculty members’ roles  comprised of instruction, research, and other responsibilities. The course  and enrollment data covered academic year 2015-16—courses taught  during fall term 2015, spring term 2016, and summer term 2016. Most  faculty data are from fall 2015. For some faculty who were not in the fall  2015 data file because they started teaching in spring 2016, for instance,  USG matched fall 2016 faculty data to the course data. Earnings data  covered calendar year 2015 and included earnings year-to-date through  November. North Dakota Postsecondary  Institution Administrative Data  (NDUS data) The data from NDUS officials covered all non-tribal 4-year and 2-year  public institutions in North Dakota identified in our IPEDS universe and  included course and enrollment data, as well as faculty and earnings  data. All of the data covered academic year 2015-16—courses taught and  earnings during fall term 2015, spring term 2016, and summer term 2016.  The data included common unique identifiers that allowed us to merge  extracts we received according to faculty ID and institution. The data were  downloaded by NDUS officials from a centralized data system into which  the North Dakota institutions report their data directly. Ohio Postsecondary Institution  Administrative Data (ODHE  data) The data from ODHE covered all 4-year public institutions and most 2- year institutions in Ohio identified in our IPEDS universe and included: (1)  course and enrollment data, (2) faculty data, and (3) faculty earnings  data. All of the data were from ODHE’s Higher Education Information  (HEI) system, a comprehensive relational database that includes student  enrollment, course, financial aid, personnel, finance, and other data  submitted by Ohio’s colleges and universities. The course and  enrollment data covered academic year 2014-15—courses taught during  summer term 2014, fall term 2014, and spring term 2015. Faculty and  earnings data covered fiscal year 2015 (i.e., July 2014 through June  2015). Humanities Departmental  Survey (HDS) The HDS is a collaborative effort to collect and analyze information from  humanities departments across a number of academic fields. The HDS is  sponsored by AAAS, and national humanities organizations and  disciplinary associations, such as the Modern Language Association and  the American Historical Association, helped develop the HDS. The survey  collects a variety of information for each humanities field, including data  on the number and types of faculty and students taught by faculty type.  The survey has been administered twice, covering academic years 2007- 08 and 2012-13. In both instances, the Statistical Research Center of the  American Institute of Physics administered the surveys to a nationally  representative stratified sample of humanities departments in four-year  colleges and universities that existed in 2007-08 and was updated for  new disciplines in 2012-13. The 2012-13 survey included 2,127  departments in its sample across 13 humanities fields, and its overall  response rate was 71 percent. Information about faculty referred to  employment levels as of fall 2012. We identified several other discipline-specific academic associations that  have collected or are currently collecting data on faculty makeup in their  departments, including contingent faculty. However, we did not compare  the results of other department surveys to the HDS because the response  rates in other surveys were too low to be considered generalizable or  because any observable differences in faculty composition could be  attributed to differences in survey methodology or timeframe covered. Data Reliability For each of the datasets described above, we conducted a data reliability  assessment of variables included in our analyses. We reviewed technical  documentation and related publications and websites with information  about the data. We spoke with the appropriate officials at each agency or  organization to review our plans for analyses, as well as to resolve any  questions about the data and any known limitations. We also conducted  electronic testing, as applicable, to check for logical consistency, missing  data, and consistency with data reported in technical documentation. We determined that the variables we used from the data we reviewed were  sufficiently reliable for the purposes of this report. Section 2:  Quantitative Analyses  of the Makeup,  Utilization, and  Economic  Circumstances of the  Postsecondary  Instructional  Workforce This section discusses the quantitative analysis methods (not including  regression analyses) we used to address the makeup, utilization, and  economic circumstances of the postsecondary instructional workforce.  We used federal data from CPS, IPEDS, and SDR, state data from  Georgia, North Dakota, and Ohio, and non-governmental data from HDS  for these analyses. In each of the analyses that follow, our population of analysis was  postsecondary instructional faculty. However, our definition of  instructional faculty varied depending on the data source, as different  sources provide different information regarding instructional  responsibilities. For example, IPEDS indicates whether an individual’s  responsibilities are primarily instructional whereas the state data indicates  whether an individual teaches a course. For each set of analyses, we  explain what definition of instructional faculty we used. Within our  population of instructional faculty, we defined as contingent faculty any  full-time or part-time faculty who do not have tenure or are not on the  tenure track. IPEDS Analyses of  Historical and Current  Makeup To analyze whether and how the size of the contingent faculty workforce  has changed over time, we used IPEDS data to identify instructional staff  nationwide by type of institution in 1995, 1999, 2003, 2007, 2011, and  2015, which is the most recently available year of data. The five historical  snapshots used data from the fall staff surveys to examine counts of  faculty and any trends in postsecondary education during the period  1995-2011. The 2015 snapshot used data from the “employees by  assigned position” survey to examine current counts of faculty by position  type and used data from the fall staff survey to examine counts of faculty  by gender and race. We could not compare the historical and current  snapshots of faculty counts due to a significant change in 2012-13 to how  IPEDS defines instructional staff. Prior to this change, instructional staff  included those “whose primary responsibility is instruction, research,  and/or public service” combined in a single category. After the change,  instructional staff included only those whose responsibilities are primarily  instructional or those “for whom it is not possible to differentiate between  instruction or teaching, research, and public service because each of  these functions is an integral component of his/her regular assignment.” As a result, data on instructional faculty collected since 2012 is not  comparable to data collected prior to 2012. For each of these years of faculty data, we merged information from the  IPEDS institutional characteristics file and focused our analyses on a  universe of institutions that fit as close as possible to the following  definition: Active, Title IV, degree-granting 2-year and 4-year primarily  postsecondary institutions that are generally open to the public, have  at least 15 full-time equivalent staff, and reported at least 1  instructional staff member or graduate teaching assistant. The number of postsecondary institutions can change from year to year  due to new schools opening or existing schools closing or consolidating  with other schools, as well as due to changes in how schools elect to  report data to IPEDS. Not all of the same variables were available in the 1999 and 1995 IPEDS  institutional characteristics files. As a result, for the 1999 data, we used  different variables that also identified institutions that fit this definition. For  the 1995 data, we approximated this definition by identifying institutions  that offered at least an associate’s degree or higher and that were active  institutions eligible for student financial aid (to approximate Title IV  institutions). For the historical snapshots, we identified counts of faculty by institution  type (i.e., control: public, private, for-profit; and level: 2-year, 4-year). We  categorized faculty according to the following position types:  full-time tenure-track (both tenured and non-tenured but on a tenure  track); part-time; and graduate teaching assistant. The historical IPEDS data (from the fall staff surveys) do not break out  part-time tenure-track from part-time contingent. For the 2015 snapshot, we identified counts of faculty by institution type,  as well as by other institutional characteristics, such as size and the  highest degree offered by the institution. We categorized faculty  according the following position types:  full-time tenure-track (both tenured and non-tenured but on a tenure  track); part-time tenure-track (both tenured and non-tenured but on a tenure part-time contingent; and graduate teaching assistant. We also identified contingent faculty positions by their contract types: non-faculty status. We used the 2015 IPEDS fall staff survey data to identify faculty by  gender and race/ethnicity group. For full-time faculty, we were able to  examine the full spectrum of tenure-track versus contingent with various  contracts. However, because these data were from the 2015 IPEDS fall  staff survey, the data do not break out part-time tenure-track from part- time contingent. The IPEDS race/ethnicity categories we analyzed were: Black or African American Other or unknown (includes the IPEDS race/ethnicity categories:  American Indian or Alaska Native; Native Hawaiian or other Pacific  Islander; two or more races; and race/ethnicity unknown) White (non-Hispanic) Aggregated IPEDS data represent the universe of postsecondary  instructional faculty positions, rather than a mutually exclusive count of  unique instructional faculty members. IPEDS data are reported at the  institution level, and so for any given institution the counts they report  represent both the number of faculty at the institution and the number of  positions they fill. However, because faculty who teach at more than one  institution are counted and reported by each institution, when faculty  counts are aggregated across multiple institutions, these faculty are  counted multiple times—for each position they fill. As a result, aggregated  counts based on IPEDS data represent the universe of unique  instructional faculty positions, rather than the universe of unique faculty  workers. CPS Analyses of Current  Faculty Makeup and  Economic Circumstances We used CPS data from the March 2016 ASEC to estimate the numbers  of workers employed as postsecondary teachers in colleges and  universities nationwide during calendar year 2015. We categorized as  postsecondary instructional faculty any worker whose employment was in  both the “postsecondary teachers” occupation (census code 2200) and  the “colleges and universities, including junior colleges” industry (Census  code 7870). We also determined whether a worker was employed full- time (35 hours or more) or part-time (less than 35 hours) using another  variable in the ASEC. Among other differences with IPEDS data (see discussion of IPEDS  above), CPS data capture the number of workers rather than the number  of positions in postsecondary education and counts each worker once  even if they work at multiple institutions. In addition, because CPS  represents the entire labor force, the data include workers at  postsecondary institutions that we may have excluded from our IPEDS  analyses (e.g., non-degree-granting institutions). We utilized CPS data  to provide context for the total number of postsecondary teachers and to  estimate the proportions of the instructional workforce represented by full- time and part-time faculty. However, analysis of CPS data was not a  primary component of our report because the data cannot differentiate  workers by institution or by tenure status. As a result, the estimated  population of full-time faculty includes both tenure-track and contingent  faculty. Because CPS identifies workers as opposed to positions (which  might yield a lower count than the IPEDS data) and includes workers at  postsecondary institutions that we excluded from our IPEDS analyses  (which might yield a higher count than the IPEDS data), the count of  workers in the CPS data and the count of positions in the IPEDS data are  not directly comparable. We also examined the reasons part-time faculty reported they worked  part-time. We focused our analysis on 3 groups of part-time faculty: (1)  those who reported wanting to work part-time; (2) those who reported  they could only find a part-time job; and (3) those who reported seasonal  or temporary fluctuations in the availability of employment (i.e., “slack  work”)—we combined the latter two groups because they are both related  to economic circumstances. To analyze the economic circumstances of contingent faculty, we used  CPS data to estimate the median earnings of full-time and part-time  faculty, as well as their receipt of work-provided retirement and health  benefits. Our analysis of median earnings used ASEC data on the self- reported amount earned from a worker’s employer before deductions. In  examining benefits, we used the term “work-provided” rather than  “employer-sponsored” because the ASEC survey questions ask about  benefits offered by a worker’s employer or union. For our analysis of  access to work-provided retirement plans, we counted a worker as having  a work-provided retirement plan if they responded “yes” to both of the  following questions from the ASEC: (1) “Other than Social Security, did  the employer or union that  worked for  have a pension  or other type of retirement plan for any of the employees?” and (2) “Was   included in that plan?” We also estimated the percentages of full- time and part-time faculty who were covered by any private health  insurance plan; were covered by private health insurance in their own  name; or had a work-provided health insurance plan. Those individuals  without insurance could have received insurance coverage through a  family member or other means. SDR Analyses of  Compensation and  Employment Experiences To compare—at the national level—the compensation and employment  experiences of contingent faculty and tenure-track faculty, we used 2013  SDR data to identify different faculty types and examined the extent to  which there were differences in earnings, benefits, and job satisfaction.  SDR data only include doctorate holders in STEM, health, and social  sciences fields, and thus our estimates cannot be generalized to non- doctorate holders or to fields outside of STEM, health, and social  sciences fields. For that reason, we did not present faculty population size  estimates using SDR data. We created our analysis population of instructional faculty based on  responses to questions regarding work activities and institution type.  Using these variables, we classified as instructional faculty any  respondents who said that their “primary or secondary work activity is  teaching,” and whose institution type was a 2-year college; 4-year college  or university; medical school; or university-affiliated research institute.  This resulted in an analysis population of 7,232 instructional faculty  respondents; however, our analyses are weighted analyses that  generalize to the population. Within our analysis population, we identified faculty types based on  tenure status (i.e., tenured/on the tenure track or not on the tenure track)  and whether respondents said they worked 36 hours or more per week or  less than that (i.e., full-time versus part-time). We categorized graduate  assistants separately, though we chose not to present estimated  percentages for graduate assistants. Given that SDR is a survey of  doctorate holders, it may be that graduate assistants in the SDR data  are—for example—working toward another doctoral degree or have  remained at their degree-granting institution in a postdoctoral position. In  either case, we believe the working arrangements and economic  circumstances of these individuals may be unique from those of most  other graduate assistants. Without more detailed information, the data  do not allow us to determine the exact nature of graduate assistant  positions in the SDR data or explain how they compare to other types of  positions. We also chose not to present estimated percentages for part- time tenure-track faculty given that they represented a small proportion of  our analysis population. To analyze the economic circumstances of contingent faculty, we used  SDR data to calculate median annual earnings by faculty type, as well as  data on the availability of work-provided benefits. We calculated median  earnings using data on basic annual salary from the respondent’s  principal job. We analyzed data on the following types of benefits: health  insurance, pension or retirement plans, profit-sharing plans, and paid  vacation/sick/personal days. Respondents were asked whether each  type of benefit was available to them regardless of whether they chose to  take the benefits. To analyze the employment experiences of contingent faculty, we used  SDR data on job satisfaction, reasons for working part-time, and  attendance of professional meetings. To examine job satisfaction, we  used data on satisfaction with overall employment, job security,  opportunities for advancement, salary, and benefits, from which we  estimated the percentage of faculty who were satisfied, somewhat  dissatisfied, or very dissatisfied by faculty type. Our analysis of part-time work first included whether a respondent who  reported working part-time said they wanted to work full-time.  Secondly,  among those who wanted—and who did not want—to work full-time, we  calculated the percentage who said they worked part-time (1) for family  reasons, (2) because a full-time job was not available, (3) because they  did not need/want full-time work, and (4) because they were a student,  had an illness, or held another job. Respondents could indicate more than  one reason for working part-time. We also analyzed a variable on attendance of professional meetings to calculate the percentage of  faculty, by faculty type, who reported attending professional association  meetings or conferences during the past 12 months. The SDR data included other variables that identify a respondent’s  academic position, such as research faculty, administrators, adjuncts, and  others. We analyzed these variables to determine whether to use them to  categorize faculty, but found that they were not the most appropriate for  our purposes. However, we observed that these variables may have  implications on the economic circumstances of different types of faculty  and so used them as control variables in two of our regression models on  annual earnings. For example, we analyzed earnings of instructional  faculty who said they were “adjunct” faculty or administrators. Among  full-time and part-time contingent faculty, estimated median annual  earnings decreased when we included only faculty who said that they  were adjunct faculty (see table 13). However, the data do not allow us to  explain how or whether the positions for faculty who identified as adjuncts  are different compared to the positions of those who did not identify as  adjuncts, and, based on our team’s interviews with administrators,  different institutions and individuals apply different meanings to the term  “adjunct.” As may be expected, among full-time tenure-track and full-time  contingent faculty, estimated median annual earnings increased when we  limited the population to only those faculty who said they were  administrators (see table 13). State Data Analyses of  Makeup and Utilization We used consistent methods to analyze data from Georgia, North  Dakota, and Ohio on faculty workforce makeup and utilization, though we  analyzed the data from each state separately. In addition, while each  state dataset was structured slightly differently, used different variable  names, and contained some unique elements or ways of capturing  information about faculty or courses, we restructured and compiled the  information to provide consistency across the states. In the state data, we identified instructional faculty as any individual who  taught a course during the given academic year. This definition includes a  variety of staff (e.g., deans, administrators, coaches, research faculty,  and postdocs) who fill about 2-10 percent of positions, depending on  institution type and state. In addition, instructional graduate assistants— who are listed in the state data as instructors of record—fill about 8 to 15  percent of positions at 4-year institutions in the three states. Each state’s data were ultimately structured as a set of unique faculty- institution pair observations—where faculty were listed once, by their  employing institution. Each faculty-institution pair observation had  variables describing the faculty member’s and institution’s characteristics,  as well as counts of courses, students, and student credit hours taught by  the faculty member at that institution (including by academic term and by  course characteristics). Faculty Data Compilation and  Restructuring For all three state datasets, we coded and grouped certain faculty  characteristics variables, including academic rank, age group,  race/ethnicity, sex, and tenure status, to ensure consistency across  states. For example, in coding tenure status, we consistently categorized faculty as “non-tenure-track” if they were identified in the  source data as not in a tenure-track position, as having been denied  tenure, as being in some other status, or as being in a position for which  tenure was not applicable. Some faculty characteristics variables were  structured differently in each of the three states and thus required unique  methods of recoding, though we applied consistent approaches and logic  in each case (see table 14). We also identified each individual’s academic discipline based on  information provided in each state’s data about their department. Faculty  members’ departments in the Georgia and Ohio data are identified by  their standardized Classification of Instructional Programs (CIP) code.  The North Dakota data did not include the CIP code for faculty members’  departments and department names in the North Dakota data were not  consistent across institutions. Thus, we coded North Dakota departments  by matching them manually to corresponding CIP codes. After manually assigning CIP codes to faculty in the North Dakota data,  we identified the highest level 2-digit CIP code for each faculty member in  all three state datasets. However, because the 2-digit CIP code  identifies over 40 fields of study, we grouped these by academic  discipline for our analyses. To group departments, we used a crosswalk  provided by Ohio that listed CIP codes according to 12 possible  disciplines they were most closely associated with. Although the  Department of Education’s CIP coding system does not include a  commonly accepted list of disciplines, we determined that Ohio’s  convention was reasonable and we applied the coding consistently  across all three states to identify the academic discipline of each  individual. The North Dakota data included multiple observations for some faculty  members within a single institution and term. This occurred for a variety of  reasons, such as a faculty member holding two positions at the same  institution (e.g., both a coach and an instructor, or half time as an  instructional graduate assistant and half time as a research graduate  assistant). To compile a consistently structured dataset of unique faculty- institution pair observations, we implemented the following sequential  process to select and eliminate duplicate faculty observations. We  confirmed with North Dakota officials that our approach and methods  were appropriate. For faculty with multiple observations, we dropped any observations  where (1) no earnings were listed in any term or earnings were only  listed for the summer term but the faculty member taught no courses  at the given institution in the summer; or (2) the work responsibilities  associated with the faculty observation were not directly related to  teaching (e.g., graduate assistant research or grading, management,  administration, research, or coaching) and a different observation for  that faculty member at the same institution had teaching duties listed.  We dropped these duplicate observations because there was a more  appropriate observation to be used for the given faculty member at  the given institution with earnings information and an associated  instructional position. For the remaining faculty with multiple observations, we sequentially  kept one observation as the primary faculty position based on  hierarchical logic we developed. For example, we dropped any  additional observations with an employee status other than “active” or  a position identified as “temporary.” As appropriate, we either  aggregated hours worked and earnings across the multiple  observations before dropping the duplicate observations or we took  the hours worked and earnings values from the observation identified  as primary. Course Data Compilation and  Restructuring Course data from all three states included each unique course section  taught over the academic year by institution, term, and faculty instructor.  We analyzed course sections for which there was an instructor identified  and enough information about that faculty member to categorize them by  faculty type (e.g., full-time tenure-track versus part-time contingent,  etc.). For all three states, we aggregated these data by course type and  other information to the level of the unique faculty-institution pair. For  example, a single faculty member at a single institution may have taught  10 course sections, all at the undergraduate level and spread across the  year—4 in fall term, 4 in spring term, and 2 in summer term. Courses are  listed in the state data at both the course number level (e.g., Biology 101)  and the course section level (e.g., Biology 101, Sections A, B, and C).  Our analyses generally examined unique course sections by faculty  member (e.g., two separate sections of Biology 101 are considered as  two courses), as that is a more accurate depiction of faculty workload.  Thus, for consistency and clarity throughout our report, we use the term  “courses” to refer to our analyses of course sections. In a few special  circumstances, we counted courses at the course number level instead of  the course section level to minimize potential bias in our work (see  additional information below). The course data included information about courses that we  systematically coded and grouped to ensure consistency across the three  states. For example, each state identified the academic level of each  course. The Georgia and North Dakota data identified courses along a  spectrum—generally developmental, freshman, sophomore, junior,  senior, or graduate. The Ohio data had a different classification series: Developmental: All courses which are below college level General Studies: All courses which are general, introductory, or core Technical: Only those courses which are part of an associate degree  program of technical education and are within the technical portion of  a curriculum Baccalaureate: All courses which are specialized within a discipline Master’s / Doctoral / Professional – All graduate courses of various  To categorize undergraduate course levels consistently across the states,  we identified courses as (1) undergraduate lower if they were at the  freshman, sophomore, general, or technical levels; or (2) undergraduate  upper if they were at the junior, senior, or baccalaureate levels.  Developmental and graduate courses were identified consistently in each  state’s data. We made a number of decisions about how to categorize and count  courses consistently across institutions and states. For example, we  dropped cancelled courses or courses with no student enrollment. We  also excluded from our primary analyses courses that would likely be  student-led or student-initiated and thus could be considered atypical  courses. We excluded these courses to minimize the potential bias of  inflating the percentage of courses taught and deflating the earnings per  course of one faculty type relative to another. After reviewing course  types and titles, as well as associated student enrollment numbers and  credit hours, we identified courses that met this definition and categorized  them as atypical. Among the courses we identified as student-led or  student-initiated were: Art or musical exhibitions, performances, or recitals  Independent, supervised, dissertation, or thesis research  Internships, fieldwork, practicums, cooperative experiences Varsity athletics  These atypical courses made up close to a quarter of all courses across  4-year institutions in the three states and less than 10 percent of courses  at 2-year institutions. As expected, and due to many being independent or  single-student enrollment courses, they generally represented much  smaller proportions of student credit hours across all institutions. Across  4-year public institutions in all 3 states, tenure-track faculty taught close to  75 percent or more of these courses. We also accounted for cross-listed courses and multiple lab sections to  more accurately capture faculty workloads. Some courses in the Georgia  and North Dakota data were cross-listed in multiple departments with  different course acronyms for each department. For example, the course  “Intro Robotics Research” taught by a single faculty member at one  institution was listed three times under different department acronyms,  with several students enrolled under each listing. Course sections listed  multiple times due to being cross-listed would artificially inflate counts of  courses taught, as these cross-listings actually represent only one course  section. To avoid inappropriately counting them as separate courses, we  counted cross-listed courses by using their course numbers (and also  their course name in North Dakota) without the course acronyms  attached. Thus, when we aggregated counts of courses by faculty- institution pair, term, and course type, these cross-listed courses were  counted as one course and numbers of students and student credit hours  were aggregated in association with the course. Due to inconsistencies in  how lab sections were organized in the data, we aggregated labs by their  course number (within a faculty-institution pair and term). For example,  some lab sections were listed as 4-credit courses that appeared to have  the lecture and lab components combined in a single listing, while others  had a 3-credit lecture course listed and multiple sections of a 1-credit lab.  To be as consistent across states as possible and to minimize the  potential bias of inflating the percentage of courses taught and deflating  the earnings per course of one faculty type relative to another, we  combined lab sections into a single course count. To do so, we identified  the lab sections within a particular course number, instructor, institution,  and term and then flagged the first lab section for counting. Thus, similar  to the cross-listed courses, when we aggregated counts of courses by  faculty-institution pair, term, and course type, these lab sections were  counted as one course and enrollment numbers aggregated in  association with the course. For outlier faculty who taught especially large numbers of course  sections, we counted their courses taught at the course number level  (e.g., Biology 101) instead of the course section level (e.g., section 1 of  Biology 101). After compiling the data and producing preliminary counts  of course sections taught, some faculty in all three states emerged as  outliers—teaching large numbers of course sections in a given term, in  some cases, more than 50, for example. Though the data do not provide  exact reasons for the large numbers of course sections taught, these  outliers may have a number of possible explanations that could vary by  state and institution. Among other effects, these outlier observations  could artificially inflate the percentage of courses taught and deflate the  earnings per course of one faculty type relative to another. To mitigate  these effects, we counted courses taught for these outlier faculty at the  course number level—where they are clearly distinct—instead of the  course section level—where it is less clear why there are multiple  sections. For example, Biology 101 is clearly a different course than  Biology 201 or Chemistry 101 (regardless of section number), whereas  section A of Biology 101 could actually be combined with section B and  they are just listed separately for other reasons. We did not set a  maximum number of courses that an individual could teach (i.e.,  individual faculty could still be listed as teaching large numbers of courses  if they were associated with large counts at the course number level). We  counted course numbers for outlier faculty because their large numbers of  course sections listed suggested the possibility of a data anomaly; for all  others (non-outlier faculty), we counted course sections. We set our  outlier threshold as 15 course sections taught over the academic year  based on an examination of the range of course sections taught by faculty  in the three states’ data and conversations with administrators during our  site visits. According to preliminary counts of course sections taught after  excluding atypical courses, more than 95 percent of faculty in each state  taught 15 course sections or fewer over the entire academic year. In  addition, during our site visits, the largest number of course sections  taught per term that administrators identified was 6, which could  reasonably result in 15 course sections over the year (6 in fall term, 6 in  spring term, and 3 in summer term—half the amount due to the  condensed format). Analysis of Faculty Makeup  and Utilization To analyze faculty makeup and utilization by institution, we merged  information about institutional characteristics from IPEDS onto our state  datasets. We analyzed faculty makeup, including counts and percentages  of faculty positions by type of position and faculty characteristics (e.g.,  age, education, and academic discipline), by the following faculty  categories (based, in part, on faculty tenure and work statuses):  We sometimes analyzed full-time and part-time contingent faculty and  instructional graduate assistants combined as “contingent faculty” and  full-time and part-time tenure-track combined as “tenure-track faculty.”  Unlike our analyses of IPEDS data, we included instructional graduate  assistants in our combined contingent faculty group because they were  listed as teachers of record for courses in the state data. We analyzed  administrators/management as a separate group because these  individuals represent a non-traditional class of faculty. For example,  administrators may not have tenure-track status due to their management  roles, but are in positions that may not be appropriate to be considered  “contingent” (e.g., a dean might not be a tenure-track faculty member, but  neither are they a contingent faculty member). We analyzed educational  attainment of faculty by calculating the percentage of faculty with  graduate or doctoral degrees by faculty type and institution type in in  North Dakota and Ohio. Table 15 shows the total number of instructional  faculty positions by institution type in each state, as well as selected  faculty demographics. We analyzed faculty utilization by aggregating counts of courses,  students, and student credit hours taught by each faculty category above,  and by term and type of course, and by calculating percentages taught  out of the entire population and certain subgroups. As a first step in this  process, we aggregated counts of courses, students, and student credit  hours for each faculty-institution pair by term and type of course. As a  result, each faculty-institution pair had count variables that listed, for  example, how many courses and students they taught in fall term at the  undergraduate upper level. The Georgia and Ohio data listed courses  multiple times if multiple faculty share the instructional responsibility. To  ensure course sections were not double-counted, we counted them in  fractional terms based on how many instructors were listed; for example,  if a course section was listed twice—with two faculty members having  equal responsibility for the course—we counted each faculty member as  teaching half of that course. We also used this fractional count to pro-rate  or assign responsibility for students and student credit hours. We  calculated this fractional count slightly differently for the Georgia and the  Ohio data: Georgia: The Georgia data provided a teaching responsibility  percentage for each faculty member associated with a course section.  For example, a course section that was listed 3 times (for 3 different  faculty with responsibility) might be split evenly 1/3-1/3-1/3 or might be  split as 50-30-20 percent responsibility to each of the three faculty  members. Thus, we used this individually provided fractional value. Ohio: The Ohio data did not provide a teaching responsibility  percentage for each faculty member associated with a course section.  Thus, we assigned equal responsibility (as the simplest assumption)  to all staff listed for a course. After aggregating counts to the faculty-institution pair level, we further  aggregated counts to the faculty category and institution type level. Our  analyses focused on counts and percentages of courses and student  credit hours by these faculty categories. Table 16 shows the total  number of courses taught by institution and faculty types in each state. We also analyzed economic circumstances by examining median annual  earnings and receipt of work-provided retirement, health insurance, and  life insurance benefits by faculty type. We calculated an annual earnings  amount for each faculty member and then analyzed median earnings by  faculty type. For benefits, we identified whether individual faculty  received a given benefit during the year, and then calculated the  percentage of each faculty type receiving those benefits. We were unable  to analyze benefits in this way for faculty in Ohio. See table 14 above for  additional details about our earnings and benefits calculations by state. HDS Analyses of Faculty  Makeup To estimate population percentages by faculty type and discipline in  humanities departments at 4-year institutions, we used HDS data that  were published in a technical report sponsored by AAAS. Our  population of instructional faculty included faculty in humanities  departments at 4-year institutions. The sample was stratified by  discipline and degree level of courses taught (i.e., bachelor’s, master’s,  and doctoral degree courses). We were unable to access the data with  the sample design information (i.e. sampling weights and stratification  identifiers) necessary to calculate margins of errors that took into account  the sample design features. To allow us to estimate margins of error for  the estimates presented in the report, AAAS provided information on the  number of respondents associated with each response category since the  survey had unit and item nonresponse. We incorporated this information  into a simple random sampling formula, which we adjusted for the design  effect due to unequal weighting that resulted from stratification within  departments (e.g., differences in the extent to which departments may  offer bachelor’s, master’s, and doctoral degree courses). Section 3: Pay per  Course Regression  Analysis (State Data) This section discusses the regression analysis methods we used to  analyze and compare pay-per-course rates across different types of  faculty at public institutions in North Dakota and Ohio. We used data from  the three states to conduct multivariate regression analyses that  examined rates of compensation across faculty types. Data from North  Dakota and Ohio allowed us to link faculty members’ pay over the course  of an academic year with the number of courses they taught to calculate  pay-per-course rates that are comparable across faculty types. Data from  Georgia did not allow us to do this because the earnings data from  Georgia is for a calendar year that did not align with the course data for  the academic year. However, we used Georgia’s data to develop  assumptions about faculty work activities (see below for more details).  The state data we used to analyze pay-per-course rates covered courses  taught and earnings from fall 2015 through summer 2016 for North  Dakota, and summer 2014 through spring 2015 for Ohio. Analysis Population The faculty populations included in our regression analyses of the North  Dakota and Ohio data begin with the same population of instructional  faculty analyzed elsewhere in our work—any individual who teaches a  course at a 4-year or 2-year public institution in the state. However, due  to some faculty observations missing information for independent  variables, as well as the specifications of some of our models that  focused on subgroups within the data, the number of faculty observations  in our regression analyses differed slightly from those in our other  analyses. In assessing the association between faculty type (e.g.,  contingent faculty) and pay per course, we focused on three primary  populations: (1) all faculty; (2) primarily teaching faculty; and (3) primarily  teaching faculty at 4-year institutions. The primarily teaching faculty group  excludes faculty who primarily hold other roles unrelated to instruction  (e.g., administrators and research faculty). We also examined a  population limited to 4-year institutions because their pay and faculty  utilization structures may differ substantively from 2-year institutions. North Dakota: Compared to the 3,608 faculty observations with  complete faculty and course identification data across North Dakota  public institutions that we analyze for workforce makeup and  utilization, the number of observations included in our regression  analysis population is reduced to 3,486 due to our dropping of cases  where total earnings was less than one dollar or missing, or where the  number of in-scope courses taught was zero (more information below  under discussion of dependent variables). After introducing the full  range of independent variables in our complete model with all faculty  at all institutions, our population is reduced to 3,485 due to one faculty  member being omitted due to missing data. When we limit the  population to primarily teaching faculty at all institutions, there are  3,404 observations, and when we only include 4-year institutions,  there are 2,876 observations. Ohio: Compared to the 34,461 faculty observations with complete  faculty and course identification data across Ohio public institutions  that we analyze for workforce makeup and utilization, the number of  observations included in our regression analysis population is reduced  to 30,672 due to our dropping of cases where total earnings was less  than one dollar or missing, or where the number of in-scope courses  taught was zero (more information below under discussion of  dependent variables). After introducing the full range of independent  variables in our complete model with all faculty at all institutions, our  population is reduced to 30,656 due to 16 faculty members missing  data for covariates. When we limit the population to primarily teaching  faculty at all institutions, there are 28,811 observations, and when we  only include 4-year institutions, there are 21,482 observations. Approximating  Instructional Pay from  Georgia Data on Faculty  Work Activities As explained earlier in the report, we examined instructional pay per  course as a way to isolate the earnings for comparable work across  faculty types—for example, those who only teach (salaried or paid by the  course) versus those who have other responsibilities beyond teaching.  Institutions do not generally structure compensation by types of work  activities, though some faculty have work responsibility expectations  associated with their positions; for example, a full time tenure-track  assistant professor may have work responsibly expectations of 60  percent instructional, 30 percent research, and 10 percent other service  to the institution. If this faculty member earns $80,000 per year and  teaches 8 courses over the course of the year, her total pay per course,  which ignores time spent on research and other activities, would be  $80,000/8 = $10,000 per course. However, prorating the earnings to  those for instructional work activities only, the instructional pay per course  would be ($80,000*0.6)/8 = $6,000. We assessed each regression model  based on the outcomes of total pay per course and instructional pay per  course, where earnings were prorated for instructional time. Because information about faculty work activity was unavailable in the  North Dakota and Ohio data, but was available in the Georgia data, we  used empirical data that we received on four of the Georgia 4-year public  institutions to identify work activity percentages by faculty type. We then  assigned those percentages to similar faculty in North Dakota and Ohio.  We identified the median instructional work activity percentages for the  faculty in Georgia’s 4-year public institutions within profiles based on a  combination of faculty characteristics including faculty category (e.g., full- time tenure-track, full-time non-tenure-track, part-time non-tenure-track,  etc.), job category (e.g. administration/management, teaching faculty,  research/other faculty, etc.), and when applicable, rank (e.g. full  professor, assistant professor, instructor/lecturer, etc.). We then applied  the median instructional work activity percentage from the Georgia data  by these profile groups to faculty at 4-year institutions in the North Dakota  and Ohio data with the same profile. For faculty in the job categories of  administrators/management staff, instructional graduate assistants,  coaches, and postdocs, the median instructional work activity percentage  in those groups overall was sufficiently explanatory. For the remaining  two job category groups of instructional faculty and research/other faculty, we used median work activity percentages by faculty category (e.g., full- time tenure-track) and rank (e.g., full professor). If a faculty member did  not have a rank identified in the data, we used the median work activity  percentage for the faculty category overall (see table 17). Because the  data on work responsibilities pertained to public 4-year institutions in the  Georgia data, we did not prorate faculty at 2-year institutions accordingly.  Because 2-year institutions generally do not have a research mission, we  coded all faculty at 2-year institutions as 100 percent instructional, except  for administrators/management staff. We prorated  administrators/management staff according to the same method as at 4- year institutions due to their likely having substantial non-teaching  responsibilities. Faculty earnings in the North Dakota and Ohio data were multiplied by  the relevant median instructional work activity percentage in order to  adjust pay to reflect instructional work activity, resulting in an  “instructional pay” amount. The majority of adjustments—prorating of  earnings to account for non-instructional activities—were applied to  faculty in the full-time tenure-track group, who were most likely to have  other work responsibilities. Some adjustment to earnings also occurred in  the full- and part-time contingent groups, as well as for faculty who had a  job type that indicated substantial administrative and management roles.  No prorating occurred for instructional graduate assistants. Dependent Variables We conducted regressions using the following dependent variables:  a) Log (total pay per course) – In our analysis of the North Dakota and  Ohio data, we used the natural logarithm of the total pay per course,  which is defined as the total annual earnings (i.e., total pay) divided by  the total courses taught within that year.  b) Log (instructional pay per course) – In our analysis of the North Dakota  and Ohio data, we also used the natural logarithm of the instructional pay  per course, which is defined as total annual earnings adjusted to reflect  instructional work activity (i.e., instructional pay) divided by the total  courses taught within that year. We excluded cases from our analysis if they were missing values for  either total annual earnings or total courses taught within that same year  because these variables were the primary components of pay per course.  We dropped cases where total earnings were less than one dollar or  missing (19 observations in North Dakota and 2,869 observations in  Ohio) or the number of courses taught was zero (103 observations in  North Dakota and 920 observations in Ohio) since division by zero is  undefined, and our population is intended to reflect any individual who  actually teaches a course at 4-year and 2-year public institutions in the  state. We then divided pay (total or instructional) by the number of  courses taught to obtain the pay-per-course value. We use the log of total  and instructional pay per course for the dependent variables in a linear  model reflecting both the assumption that the underlying distribution is  closer to the log normal than normal, and also to present results in terms  of percentage changes in pay per course. In the Ohio data, because we use fractional counts for courses when  multiple faculty are listed as having responsibility for the course, 3,453  faculty in the analysis population teach less than 1 course. For those  faculty, we round all course counts that are less than 1 up to 1 to avoid  dividing faculty earnings by a fractional course count (between 0 and 1),  which would result in an inaccurate and substantially large pay-per- course value. Independent Variables The primary independent variable of interest in our analysis was faculty  type. We categorized faculty into five types: full-time tenure-track, full-time  contingent, part-time tenure-track, part-time contingent, and graduate  assistant. Our main interest was comparing contingent faculty and  graduate assistants to full-time tenure-track faculty. We controlled for the  part-time tenure-track group, but due to the small size of this population  (at most, 35 faculty in North Dakota and 274 faculty in Ohio), we did not  substantively examine these estimates. All regression models set the  base group for faculty type as full-time tenure-track. We included in our regression models additional independent variables as  controls for faculty and institution characteristics. Faculty characteristics  include sex, race, age, age squared (to account for the potential non- linear relationship between earnings and age), highest degree earned,  and academic discipline. Other faculty characteristics we controlled for in  our models included whether a faculty member had grant funds (North  Dakota only), whether a faculty member taught summer courses, and  indicators identifying non-traditional faculty roles, such as  administrators/management or coaches. We also included fixed effects  for institutions to control for differences between institutions, especially in  terms of pay due to factors such as size, sector, and research/graduate  component, among other things. We also examined rank of faculty (e.g. associate professor, assistant  professor, instructor/lecturer, etc.), but excluded it from our complete  models due to collinearity with the faculty type variable. Regression Model  Detailed Results Tables 18 and 19 (below) shows the coefficients and standard errors from  each of our final pay-per-course regression models, as well as for the  unadjusted model that included only the primary independent variable of  interest (total pay-per-course results at the top and instructional pay-per- course results below). For our categorical variables, estimated  coefficients are relative to the excluded (reference) category. For  example, since the reference category for our main independent variable,  faculty type, was full-time tenure-track, the estimated coefficients for other  categories of this variable are always relative to this excluded reference  category, holding all other variables in the model constant. Thus, in model  2 for North Dakota, the coefficient for full-time contingent faculty is 0.682.  This can be interpreted as full-time contingent faculty pay per course is  0.682 that of full-time tenure-track faculty (i.e., full-time contingent faculty  are paid 68.2 percent what full-time tenure-track are, per course), holding  all other variables in the model constant. Because the dependent  variables in the earnings models are the natural logarithms of earnings,  subtracting one from the presented coefficients on categorical variables  can be interpreted as the percentage change in the dependent variable  associated with a change in the categorical variable, relative to the  reference category, holding all other variables constant. In this same  example, full-time contingent faculty are paid an estimated 31.8 percent  less than full-time tenure-track faculty, because 0.682 – 1 = -0.318, or  31.8 percent less. Additional Analyses and  Sensitivity Tests The North Dakota and Ohio data used in the regression analyses include  a small number of faculty (1.1 and 0.5 percent of observations,  respectively) who are listed as teacher of record for more than 15 courses  over the year, which may represent unusually high workloads or data  anomalies. In addition, some faculty have small or large pay-per-course  values when compared to the overall distribution. To preserve the  integrity of the data, we did not exclude these observations from the  analyses. However, we tested our models with and without these  observations to assess the effect on our substantive regression results. In  order to assess the effect of faculty with a large workload, we conducted  regression models 3 and 4 (in tables 18 and 19 above) limited to faculty  who taught 15 or fewer courses over the year. In order to assess the  effect of faculty with the outermost values of the dependent variable pay  per course, we conducted the same regression models limited to faculty  whose pay per course was within the middle 98 percent of pay-per-course  values (i.e., we trimmed the bottom and top 1 percent of observations). In  both of these sensitivity analyses, we found substantively similar results. We also ran our regression models on a more refined population that only  included primarily teaching faculty at 4-year institutions (faculty at 4-year  institutions represent most of our analysis population). As shown in table  18 above, in terms of total pay per course, full-time contingent faculty in  North Dakota and Ohio are paid about 40 and 43 percent less per course,  respectively, than full-time tenure-track faculty—compared to 35 and 40  percent less per course, respectively, when both 4-year and 2-year  institutions are included. This slightly larger pay-per-course disparity as  compared to the population overall may be, in part, because pay and  utilization of full-time faculty vary somewhat by institution type (e.g., at 4- year institutions, pay is generally higher but less flat, and some full-time  tenure-track faculty teach fewer courses due to their more extensive  research responsibilities). Section 4: Annual  Earnings Regression  Analysis (SDR Data) This section discusses the regression analysis methods we used to  analyze and compare annual earnings among different types of faculty  using national 2013 SDR data on doctorate-holding faculty in the STEM,  health, and social sciences fields. Dependent Variable We conducted regressions using the following dependent variable: Log  (annual salary)—the natural logarithm of annual salary, defined as the  basic annual salary from the respondent’s principal job. Independent Variables The primary independent variable of interest in our analysis was faculty  type. We categorized faculty into five types: full-time tenure-track, full-time  contingent, part-time tenure-track, part-time contingent, and graduate  assistant. Our main interest was comparing contingent faculty to full-time  tenure-track faculty. Though we controlled for the part-time tenure-track  and graduate assistant groups, we did not substantively examine these  estimates. All regression models set the reference group for faculty type  as full-time tenure-track. We included in our regression models additional independent variables as  controls for faculty and institution characteristics. Faculty characteristics  included sex, race, age, age squared, number of weeks worked per year,  and academic discipline. Other faculty characteristics we controlled for  included the year of highest degree earned—which we used as proxy for  general experience—and whether a respondent indicated that they were  an administrator. We also included institution type (e.g., 4-year college or  university, 2-year college or university). After introducing the full range of  independent variables in our complete model, our analysis sample was  reduced from 7,232 faculty respondents to 7,226 due to 6 faculty  respondents being omitted due to missing data. We examined faculty rank (e.g. professoriate, instructor/lecturer) and  academic position variables for “adjunct” faculty and postdocs, but we  excluded these variables from our complete model, as we determined  they did not have meaningful information for the purpose of our  analyses. Regression Model  Detailed Results In our complete model, full-time and part-time contingent faculty earned  22 percent less and 70 percent less, respectively, than full-time tenure- track faculty annually (see table 20). Across our preliminary models (not  shown below) and complete model, the coefficients related to our main  independent variable remained relatively constant, ranging from 0.76 to  0.86 for full-time contingent faculty and 0.26 to 0.43 for part-time  contingent faculty, expressed as proportion of full-time tenure-track  faculty earnings. Appendix II: IPEDS Data on the Racial and  Ethnic Distribution of Faculty Positions  Nationwide, 2015 Appendix III: GAO Contact and Staff  Acknowledgments GAO Contact Staff  Acknowledgments In addition to the contact named above, Nagla’a El-Hodiri (Assistant  Director), Nisha R. Hazra (Analyst-in-charge), Sandra Baxter, Justin  Gordinas, Michael Kniss, and Alexandra Squitieri made key contributions  to this report. Also contributing significantly to this report were Melinda  Cordero, Grant Mallie, Jean McSween, Moon Parks, and Sonya  Vartivarian. Key support was provided by James Ashley, James Bennett,  Grace Cho, Jessica Orr, James Rebbe, Almeta Spencer, and Elaine  Vaurio.
Contingent faculty play a large role in postsecondary education but may not have the same job protections as tenured or tenure-track faculty. In 2015, GAO reported that contingent workers—those in temporary, contract, or other non-standard employment arrangements—earn less, are less likely to have work-provided benefits, and are more likely to experience job instability than standard workers. GAO was asked to examine issues related to contingent faculty. This report examines (1) what is known about the makeup and utilization of the postsecondary instructional workforce; (2) the roles different types of faculty fill at selected institutions and the factors administrators consider when determining faculty makeup; (3) what is known about how economic circumstances compare across different faculty types; and (4) what contingent faculty members report as advantages and disadvantages of their work. GAO analyzed data from nationally representative sources and from public institutions in three states—Georgia, North Dakota, and Ohio. GAO selected these states based primarily on data availability. GAO interviewed administrators from 9 postsecondary institutions in these states and one large for-profit institution. GAO selected institutions based on factors such as institution size and percent of contingent faculty. GAO also conducted 21 discussion groups with contingent faculty. The Department of Education did not have comments on this report. The National Science Foundation provided technical comments, which we incorporated, as appropriate. According to 2015 Department of Education data, contingent faculty—those employed outside of the tenure track—made up about 70 percent of postsecondary instructional positions nationwide, though this varied by type of institution. In addition, data from three selected states show that contingent faculty teach about 45 to 54 percent of all courses at 4-year public institutions, and higher proportions at 2-year public institutions. In terms of job stability, some full-time contingent positions with annual or longer contracts may be relatively stable while part-time positions with short-term contracts may be among the least stable, though it is unknown whether faculty in these positions have other employment. In contrast, tenure-track positions are often viewed as having a high degree of job security that is somewhat unique to postsecondary education. Administrators GAO interviewed at selected postsecondary institutions said full-time contingent faculty generally carry heavy teaching loads, and some also take on additional responsibilities, such as conducting research or advising students. However, administrators stated that part-time contingent faculty generally focus solely on teaching. As shown in the figure below, administrators also described factors they consider in determining their institution's faculty makeup. GAO examined recent data from North Dakota and Ohio public institutions and found that, among faculty who primarily teach—which excludes individuals such as administrators or researchers—part-time and full-time contingent faculty were paid about 75 percent and 40 percent less per course, respectively, compared to full-time tenure-track faculty. This comparison includes earnings for all of their responsibilities, including teaching and any other duties. However, when estimating faculty earnings for teaching duties only, pay disparities decreased to about 60 percent and 10 percent less per course for these contingent faculty, respectively. In addition, state and national data also showed that relatively few part-time contingent faculty received work-provided health or retirement benefits. In discussion groups with GAO, contingent faculty cited advantages such as the flexibility to balance professional and personal responsibilities, skill development, or working with students, and described disadvantages that included uncertainty due to short-term contracts, untimely contract renewals, and pay—including a lack of compensation for some of their work. Other concerns they cited included limited career advancement opportunities, not having a voice in institutional decision-making, and not having certain types of institutional support.