Customer cost and time investment make decisions about postsecondary enrollment among the most important in the lives of many people and their families. Many factors may influence an individual’s decisions regarding postsecondary education: what to study, where to enroll, and the level of degree to obtain. However, what is frequently unavailable to facilitate choice is information about the formal education process and the world of work most people enter coincidentally, and information about post-graduation employment and earnings. Forming reasonable expectations about the context of the education and work experience is essential to realistic strategies for educational success and goes well beyond information in the course catalogue. Currently in Wyoming, very few sources exist that link education and labor market outcomes. The Research & Planning (R&P) section of the Wyoming Department of Workforce Services produces reports in tabular and interactive graphic form, and supporting documentation allowing customers to better understand a great deal more about the career pathways between work and education leading to a greater probability of success in their chosen field of study.
With such an abundance of data provided in the tables, some fields and variables may be unclear to users; this introduction aims to provide explanation of the tables and decisions made during their development.
Background Discussion, What the Data Measure, and How They Can be Used
How important is pre-graduation work experience to the probability of post-graduation work in a field for which individuals trained? What proportion of students finance their education with work? How much do they work, and does work impede graduation? The first step in answering these and other complex questions involves assembling the relevant data and describing how work, training, and education fit together. As individuals progress from young adulthood into postsecondary education and the labor market, the alternative pathways available make measuring progress more complicated than it may first appear.
There is a growing body of literature describing the interaction between employment and participation in formal education and training programs. Typically, market outcomes are measured using worker earnings as reported to the state Unemployment Insurance (UI) program. Workers earn quarterly wage credits, which determine their eligibility for UI weekly benefit payments should they become unemployed. Quarterly wage credits are referred to as wage records.
Wage records in most states represent the level of compensation during a calendar quarter rather than a more familiar measure of earnings, such as an hourly rate of pay or monthly salary. Consequently, because the hire date and exit date from jobs can occur at any time during the quarter, the interpretation of wage records earnings measures is less than straightforward. When the goal for training programs is to earn a clearly defined family-sustaining wage, near term post-training wage records earnings levels prove difficult to interpret because people enter and leave jobs within a quarter, rather than at the beginning or end. One solution to the problem of interpretation is to focus on earnings with the same employer over a longer period of time; another solution is to develop or collect information on rates of compensation as part of administrative employment records.
The tabulations and interactive graphics referenced in this report include earnings using wage records, as well as hourly rates of compensation and the median number of hours worked per quarter. The availability of rates of compensation not only yields a more customary measure, it also allows us to ascertain the extent to which earnings change over time as individuals add human capital through maturity and as they shift hours from time spent in the classroom to time spent employed.
Researchers, and sometimes individuals and organizations with little formal education in the social and behavioral sciences, are increasingly accessing wage records, linking them to student files, and publishing average earnings post-training results. Often these calculations are carried out without reference to the socioeconomic context or human capital assets of training participants, leaving the results uninterpretable except by those whose imaginations are unimpeded by the possession of a relevant academic discipline. The tabular data presented along with earnings outcomes, such as post-graduation industry of employment, are among the most relevant contextual factors in establishing what outcomes mean. However, these tables by themselves are inadequate to anticipating future outcomes.
This report introduces consumers to tabulations and interactive graphics (see Figure 1) of student outcomes and pre-graduation work experiences. Popular terms refer to these longitudinal measures as the labor supply pipeline or components of career pathways. However they are referenced, what is clear is that most people work in their pre-graduation school years. As can be seen on page 160 of the table for the University of Wyoming 2008/09, 2009/10, & 2010/11 cohort, 73.7% of graduates with a bachelor’s degree worked a UI covered job for at least some time four years prior to graduation. For awards involving an older population of graduates, even greater proportions are found working. For the cohort of UW graduates in Dental Support Services and Allied Professions (CIP 5106), 56.9% were age 25 or older at the time of graduation and 80.4% were found working four years prior to graduation. Whether pre-graduation work experience (or on-the-job training, as can be seen in the tabular data) is highly important to post-graduation outcomes can be determined by forms of analysis beyond the descriptive tables presently available. However, it is clear that the work to date to bring the variables together in one data set is a pre-cursor to that analysis.
Because of the relatively small number (N) of students who graduate from a postsecondary education institution in Wyoming, graduates from three consecutive school years have been aggregated to form cohorts, such as the 2008/09, 2009/10, & 2010/11 cohort (see Box). Combining school years allows R&P to disclose more information and also see trends in the data over time that a small sample size (n) may not display. For example, as can be seen in the tables, where n is large, hours worked and the average hourly rate progress in a reasonable manner over time. On the other hand, when n is small, change over time is much more likely subject to rand, erratic, and inexplicable events from one period to the next.
Using the tables to make decisions about a course of study is complicated by the fact that some variables, such as gender and out-migration, are highly (non-randomly) associated with selected courses of study. What may be a reliable outcome for one gender may not be a reliable outcome for another. Moreover, it is also apparent that graduation by itself is not sufficient for the achievement of a particular outcome in the market. Graduates with a nursing degree who work in retail trade earn far less than graduates working in health care. Some fields of study require migration out of Wyoming to attain the highest level of earnings. Graduation by itself is insufficient to understanding employment and earnings outcomes, since it is clearly not the only associated variable. Confidently predicting the outcome of an educational pursuit is further complicated when the economy is neither as robust nor diversified as we would wish it to be. There are some steps which can be taken to add certainty to the decision making process. The primary link between the tables and greater certainty is the industry of employment following graduation (and secondarily, geographic location).
Tables of employment and earnings by industrial sector are published each calendar quarter by county. These tables can be used to plot local trend lines in employment sectors that employ graduates. Links to these tables can be found at http://doe.state.wy.us/LMI/toc_202.htm and https://www.bls.gov/cew/.
To better understand the outcomes of training programs in the workforce, the Bureau of Labor Statistics and the National Center for Education Statistics developed a Classification of Instructional Programs (CIP) to Standard Occupational Classification (SOC) crosswalk1. The crosswalk allows users to explore the different training programs available and the occupations for which the training programs prepare students. Students and postsecondary education institutions may also make decisions based on the characteristics of the occupations, such as employment and average wage, associated with selected CIP codes. Occupations are matched to CIP codes using crosswalks to understand the occupational outcomes of students who graduated from different degree programs. The CIP to SOC crosswalk can be found at https://www.bls.gov/soc/soccrosswalks.htm.
Occupational Employment Statistics (OES) provide additional data to understand student outcomes. An occupation refers to a specific task or set of job tasks. Consider the occupation of roustabouts, oil & gas. The Standard Occupational Classification (SOC) manual defines the occupation as follows: “assemble or repair oil field equipment using hand and power tools. Perform other tasks as needed” (Office of Management & Budget, 2000, p. 173). A single occupation may be present in a variety of industries. For example, accountants, in addition to working in accounting firms, may also work for mining companies, hospitals, state or local government, and a host of other industries. Staffing patterns within industries can also provide more information for students on the differences in employment and wages of occupations among the different industries. Information on how occupations differ among industries can be found at http://doe.state.wy.us/LMI/
LEWISSept2016ECI/toc001.htm and other OES data can be found at http://doe.state.wy.us/LMI/OES_toc.htm and https://www.bls.gov/oes/.
Long- and short-term industry and occupational projections predict trends on industry or occupational future demand. Projections are useful when examining future growth or contraction of specific industries and occupations in Wyoming. Considering projections while examining data from R&P’s student outcome tables from five years prior to graduation and five years after graduation allows users a more advanced understanding of the results of decisions made regarding what to study in postsecondary education. For example, a substantial increase in annual wages occurs for Petroleum Engineers (CIP 1425) in all industries one year after graduation. This may appear to be an occupation that students should pursue to earn high wages; however, according to the 2014-2024 long-term occupational projections, the number of petroleum engineers in Wyoming is expected to decline by 120 jobs by 2024. Students who pursue petroleum engineering may be required to go out of state to find employment in their chosen field. The industry and occupational projections and articles discussing the projections can be found at http://doe.state.wy.us/LMI/projections.htm.