For the past several blog entries, we have been rummaging through the data storehouses of the US Census Bureau’s Post-Secondary Employment Outcomes (PSEO) dataset, exploring average overall earnings for bachelor’s degreeholders by institution type, as well as the degree to which bachelor’s graduates are either non-employed or marginally-employed. This new post is a continuation of our previous review of average post-graduation earnings by academic discipline across Carnegie Classifications published earlier this month. The data presented below show average post-graduation earnings by academic discipline at the institutional level, allowing comparisons across public universities in the PSEO data. This post looks at data for Doctoral Institutions based on Carnegie Classification, while data for Master’s Institutions will be presented in the next blog post. As a reminder, there are several caveats in the PSEO data, such as the earnings data being based on Unemployment Insurance (UI) wages that exclude some segments of the workforce (e.g., workers among private sector employers that are independent contractors, unincorporated self-employed, some family employees of family-owned business, certain farm workers, etc.).
We continue using the “Major Group” classification schema introduced in the previous blog post. Developed by Georgetown University’s Center on Education and the Workforce (CEW, see page 39), these Major Groups organize the multitude of academic disciplines in the PSEO data into a more manageable number of 15 instructional clusters. As with the previous presentations of the PSEO data, three time-points are included across the data visualizations below: 1, 5, and 10 years after graduation. Having 15 Major Groups with three time-points each provides us with a total of 45 earnings observations represented in the PSEO data. Out of the 58 public Doctoral Universities in the PSEO data, 44 universities have data in at least 42 earnings observations. This feature will be more important in the “Summarizing University Rankings” later in the blog post.
Due to the expansive nature of these data, we will not walk through each of the observation combinations within the narrative. However, the visualization was designed to provide the user with multiple filtering options to be able to explore the various features of the PSEO data by Major Groups within Carnegie Classifications. To enhance readability and interpretability, we have restricted the selection of Carnegie Classification and Major Group to one at a time. The default setting places Doctoral Universities: Very High Research Activity with the Agriculture and Natural Resources major as the initial view, with the data sorted by the “Year 1 Avg Earnings” column. The columns can be sorted using the “sort” icon that appears at the bottom of each column next to the column heading. Empty cells represent missing data in the original PSEO data. The fill color of the bars is associated with public university system membership for Texas institutions, as all non-Texas institutions are classified as “Out-of-State” and are filled with a light gray color.
A few observations…
In order to summarize the earnings by major group data, we ranked the universities in each of the 45 earnings observations within each of the three Doctoral-level Carnegie Classification groups. Then we created an overall average ranking for each institution across the major groups within each Carnegie Classification, which is represented in the visualization below.
Not all institutions have data for each of the 15 major groups. Also, some institutions have some data in a major group, but do not have data at all three time-points after graduation. This results in a portion of the institutions having less than the maximum total of 45 earnings observations possible in the data. To conduct the ranking analysis, we established a threshold for an institution to be included in the rankings analysis: the institution had to have earnings data in at least 33 of the possible 45 earnings observations, which is roughly 75 percent. While 58 public Doctoral Universities in the PSEO data have earnings data in at least one observation, the overall rankings data below only include 48 institutions that met the threshold. There were 20 institutions that had data in all 45 earnings observations.
As can be seen below, UT Austin had an average earnings ranking of 3.33 across the 45 observations to lead the Doctoral Universities: Very High Research Activity. Out of the 45 observations, UT Austin was ranked either first or second in 21 observations, which is almost 47 percent of the total observations. Four Texas universities (UT Austin, UT Dallas, UT Arlington, and Texas A&M University) ranked in the top 6 for overall average rankings in the Very High Research Activity group. In the High Research Activity group, four Texas universities were also in the top 6 of this group: UT San Antonio, Texas State University, UT Rio Grande Valley, and Texas A&M University-Corpus Christi. For the Doctoral/Professional Carnegie group, the top 6 universities were all Texas schools.
As we have seen in previous posts using the PSEO data, one of the greatest benefits of having multi-state data is the ability to make comparisons of post-graduation earnings using data that are not restricted to within-state data availability. This is definitely the case for the earnings data broken out by the 15 Major Groups presented above. For someone interested in how graduates from a particular program of study at their university compare to graduates from similar institutions in other states, the PSEO data allows for benchmarking to occur at a more fine-grained level than previously available. While the way we have conceptualized this blog post aggregates the data at a broader level, the raw PSEO data does include earnings data for bachelor’s degreeholders from more than 300 academic programs that are identified at the 4-digit CIP code level. This means that the capacity exists within the PSEO data to drill down further into the data to make comparisons across academic programs. For example, instead of just using the broad “Architecture and Engineering” major group above, the 4-digit CIP code level data would allow for comparisons to be made across Civil Engineering or Petroleum Engineering programs specifically.