In this section, we estimate the economic impacts stemming from the added labor income of alumni in combination with their employers’ added non-labor income. This impact is based on the number of students who have attended UHV throughout its history. We then use this total number to consider the impact of those students in FY 2018-19 and FY 2028-29. Former students who earned a degree as well as those who may not have finished their degree or did not take courses for credit are considered alumni.
The greatest economic impact of UHV stems from the added human capital—the knowledge, creativity, imagination, and entrepreneurship—found in its alumni.
While UHV creates an economic impact through its operations, research, construction, visitor, and student spending, the greatest economic impact of UHV stems from the added human capital—the knowledge, creativity, imagination, and entrepreneurship—found in its alumni. While attending UHV, students gain experience, education, and the knowledge, skills, and abilities that increase their productivity and allow them to command a higher wage once they enter the workforce. But the reward of increased productivity does not stop there. Talented professionals make capital more productive too (e.g., buildings, production facilities, equipment). The employers of UHV alumni enjoy the fruits of this increased productivity in the form of additional non-labor income (i.e., higher profits).
The methodology here differs from the previous impacts in one fundamental way. Whereas the previous spending impacts depend on an annually renewed injection of new sales into the regional economy, the alumni impact is the result of years of past instruction and the associated accumulation of human capital. The initial effect of alumni is comprised of two main components. The first and largest of these is the added labor income of UHV’s former students. The second component of the initial effect is comprised of the added non-labor income of the businesses that employ former students of UHV.
We begin by estimating the portion of alumni who are employed in the workforce. To estimate the historical employment patterns of alumni in the region, we use the following sets of data or assumptions: 1) settling-in factors to determine how long it takes the average student to settle into a career;16 2) death, retirement, and unemployment rates from the National Center for Health Statistics, the Social Security Administration, and the Bureau of Labor Statistics; and 3) state migration data from the Census Bureau. The result is the estimated portion of alumni from each previous year who were still actively employed in the region as of FY 2018-19.
16 Settling-in factors are used to delay the onset of the benefits to students in order to allow time for them to find employment and settle into their careers. In the absence of hard data, we assume a range between one and three years for students who graduate with a certificate or a degree, and between one and five years for returning students.
The next step is to quantify the skills and human capital that alumni acquired from the university. We use the students’ production of CHEs as a proxy for accumulated human capital. The average number of CHEs completed per student in FY 2018-19 was 17.0. To estimate the number of CHEs present in the workforce during the analysis year, we use the university’s historical student headcount over the past 30 years, from FY 1989-90 to FY 2018-19.17 We multiply the 17.0 average CHEs per student by the headcounts that we estimate are still actively employed from each of the previous years.18 Students who enroll at the university more than one year are counted at least twice in the historical enrollment data. However, CHEs remain distinct regardless of when and by whom they were earned, so there is no duplication in the CHE counts. We estimate there are approximately 772,242 CHEs from alumni active in the workforce.
17 We apply a 30-year time horizon because the data on students who attended UHV prior to FY 1989-90 is less reliable, and because most of the students served more than 30 years ago had left the regional workforce by FY 2018-19.
18 This assumes the average level of study from past years is equal to the level of study of students today. Emsi used data provided by UHV for a previous study to estimate students’ credit load in prior years.
Next, we estimate the value of the CHEs, or the skills and human capital acquired by UHV alumni. This is done using the incremental added labor income stemming from the students’ higher wages. The incremental added labor income is the difference between the wage earned by UHV alumni and the alternative wage they would have earned had they not attended UHV. Using the regional incremental earnings, credits required, and distribution of credits at each level of study, we estimate the average value per CHE to equal $248. This value represents the regional average incremental increase in wages that alumni of UHV received during the analysis year for every CHE they completed.
Because workforce experience leads to increased productivity and higher wages, the value per CHE varies depending on the students’ workforce experience, with the highest value applied to the CHEs of students who had been employed the longest by FY 2018-19, and the lowest value per CHE applied to students who were just entering the workforce. More information on the theory and calculations behind the value per CHE appears in Appendix 6. In determining the amount of added labor income attributable to alumni, we multiply the CHEs of former students in each year of the historical time horizon by the corresponding average value per CHE for that year, and then sum the products together. This calculation yields approximately $191.4 million in gross labor income from increased wages received by former students in FY 2018-19 (as shown in Table 2.13).
TABLE 2.13: NUMBER OF CHES IN WORKFORCE AND INITIAL LABOR INCOME CREATED IN THE COASTAL BEND, FY 2018-19
|Number of CHEs in workforce||772,242|
|Average value per CHE||$248|
|Initial labor income, gross||$191,362,047|
|Adjustments for counterfactual scenarios|
|Percent reduction for alternative education opportunities||15%|
|Percent reduction for adjustment for labor import effects||50%|
|Initial labor income, net||$81,328,870|
The next two rows in Table 2.13 show two adjustments used to account for counterfactual outcomes. As discussed above, counterfactual outcomes in economic analysis represent what would have happened if a given event had not occurred. The event in question is the education and training provided by UHV and subsequent influx of skilled labor into the regional economy. The first counterfactual scenario that we address is the adjustment for alternative education opportunities. In the counterfactual scenario where UHV does not exist, we assume a portion of UHV alumni would have received a comparable education elsewhere in the region or would have left the region and received a comparable education and then returned to the region. The incremental added labor income that accrues to those students cannot be counted towards the added labor income from UHV alumni. The adjustment for alternative education opportunities amounts to a 15% reduction of the $191.4 million in added labor income. This means that 15% of the added labor income from UHV alumni would have been generated in the region anyway, even if the university did not exist. For more information on the alternative education adjustment, see Appendix 7.
The other adjustment in Table 2.13 accounts for the importation of labor. Suppose UHV did not exist and in consequence there were fewer skilled workers in the region. Businesses could still satisfy some of their need for skilled labor by recruiting from outside the Coastal Bend. We refer to this as the labor import effect. Lacking information on its possible magnitude, we assume 50% of the jobs that students fill at regional businesses could have been filled by workers recruited from outside the region if the university did not exist.19 Consequently, the gross labor income must be adjusted to account for the importation of this labor, since it would have happened regardless of the presence of the university. We conduct a sensitivity analysis for this assumption in Appendix 1. With the 50% adjustment, the net added labor income added to the economy in FY 2018-19 comes to $81.3 million, as shown in Table 2.13.
19 A similar assumption is used by Walden (2014) in his analysis of the Cooperating Raleigh Colleges.
The $81.3 million in added labor income appears under the initial effect in the labor income column of Table 2.14. To this we add an estimate for initial non-labor income. As discussed earlier in this section, businesses that employ former students of UHV see higher profits as a result of the increased productivity of their capital assets. To estimate this additional income, we allocate the initial increase in labor income ($81.3 million) to the six-digit NAICS industry sectors where students are most likely to be employed. This allocation entails a process that maps completers in the region to the detailed occupations for which those completers have been trained, and then maps the detailed occupations to the six-digit industry sectors in the MR-SAM model.20 Using a crosswalk created by National Center for Education Statistics (NCES) and the Bureau of Labor Statistics, we map the breakdown of the university’s completers to the approximately 700 detailed occupations in the Standard Occupational Classification (SOC) system. Finally, we apply a matrix of wages by industry and by occupation from the MR-SAM model to map the occupational distribution of the $81.3 million in initial labor income effects to the detailed industry sectors in the MR-SAM model.21
20 Completer data comes from the Integrated Postsecondary Education Data System (IPEDS), which organizes program completions according to the Classification of Instructional Programs (CIP) developed by the National Center for Education Statistics (NCES).
21 For example, if the MR-SAM model indicates that 20% of wages paid to workers in SOC 51-4121 (Welders) occur in NAICS 332313 (Plate Work Manufacturing), then we allocate 20% of the initial labor income effect under SOC 51-4121 to NAICS 332313.
Once these allocations are complete, we apply the ratio of non-labor to labor income provided by the MR-SAM model for each sector to our estimate of initial labor income. This computation yields an estimated $42.2 million in added non-labor income attributable to the university’s alumni in FY 2018-19. Summing initial labor and non-labor income together provides the total initial effect of alumni productivity in the Coastal Bend economy, equal to approximately $123.5 million. To estimate multiplier effects, we convert the industry-specific income figures generated through the initial effect to sales using sales-to-income ratios from the MR-SAM model. We then run the values through the MR-SAM’s multiplier matrix.
TABLE 2.14: ALUMNI IMPACT, FY 2018-19
|Labor income (thousands)||Non-labor income (thousands)||Total income (thousands)||Sales (thousands)||Jobs supported|
|Total multiplier effect||$83,289||$40,681||$123,971||$232,609||1,279|
|Total impact (initial + multiplier)||$164,618||$82,876||$247,494||$485,240||2,493|
Table 2.14 shows the multiplier effects of alumni in FY 2018-19. Multiplier effects occur as alumni generate an increased demand for consumer goods and services through the expenditure of their higher wages. Further, as the industries where alumni are employed increase their output, there is a corresponding increase in the demand for input from the industries in the employers’ supply chain. Together, the incomes generated by the expansions in business input purchases and household spending constitute the multiplier effect of the increased productivity of the university’s alumni. The final results are $83.3 million in added labor income and $40.7 million in added non-labor income, for an overall total of $124 million in multiplier effects. The grand total of the alumni impact in FY 2018-19 is $247.5 million in total added income, the sum of all initial and multiplier labor and non-labor income effects. This is equivalent to supporting 2,493 jobs.
In order to reach 6,000 FTEs, UHV assumes a 6% growth rate in the number of students annually enrolled. Applying the 6% growth rate starting in FY 2019-20, UHV will reach 6,000 FTEs, or 9,947 students, by FY 2028-29. During this 10-year period, UHV will continue to add more and more skilled and knowledgeable alumni to the Coastal Bend workforce every year. Thus, the alumni impact will continue to grow every year until the number of UHV alumni in the Coastal Bend workforce eventually stabilizes. By FY 2028-29, there is estimated to be 1.0 million CHEs in the workforce, compared to the 772,242 CHEs in the workforce in FY 2018-19 (Table 2.13). By assuming the same breakdown of student body by education level and demographic profile, the value per CHE of $248 remains unchanged. After applying adjustments for alternative education and labor import effects, the initial labor income is $127.3 million in FY 2028-29, a significant increase compared to the $81.4 million in initial labor income in FY 2018-19.
Table 2.15 illustrates that alumni are projected to add $130.7 million in labor income and $63.8 million in non-labor income, for an overall total of $194.4 million in multiplier effects in FY 2028-29. The grand total of the alumni impact is $387.8 million in total added income, the sum of all initial and multiplier labor and non-labor income effects. This is equivalent to supporting 3,907 jobs.
By increasing enrollments steadily up to 6,000 FTEs, UHV is increasing its alumni impact by 57% between FY 2018-19 and FY 2028-29, or $247.5 million compared to $387.8 million in added income for the Coastal Bend economy. The jobs supported in will increase from 2,493 jobs to 3,907 jobs.
TABLE 2.15: ALUMNI IMPACT, FY 2028-29
|Labor income (thousands)||Non-labor income (thousands)||Total income (thousands)||Sales (thousands)||Jobs supported|
|Total multiplier effect||$130,657||$63,783||$194,440||$364,833||2,006|
|Total impact (initial + multiplier)||$257,973||$129,837||$387,810||$760,315||3,907|
Source: Emsi impact model.