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Appendix C: Updating HSU’s Economic Impact

The 2018 analysis of HSU follows a previous study conducted by ICF in 2010, which analyzed the economic impact of the California State University System on the state and key regions using 2008-2009 data. It should be noted that the previous study’s methodology is not directly comparable to the current study. Due to the differing regions of analysis in each study, different input assumptions and modeling frameworks were used. That said, it is necessary to be able to articulate the differences in the study designs and outcomes in order to interpret the results of the 2018 analysis.

Table C1 below shows a comparison of the total impacts by key output metrics excluding the alumni impact.

Table C1: Comparison of Impact Results, excluding Alumni Impact

Input Category 2010 Study*
($ Millions – 2018 Dollars)
2018 Study
($ Millions)
Industry Activity $218 $360.3
Employment 2,280 4,590
State and Local Taxes $12.2 $22.1

To understand the contrast across these results, it is important first to understand any differences in inputs. Table C2 below shows a comparison of the direct spending (model inputs) analyzed in each study for each of the three main spending categories.

Table C2: Comparison: Direct Activity (Inputs)

Input Category 2010 Study ($ Millions) 2018 Study ($ Millions)
University Operating &
Auxiliary Expenditure
$160.1 $194.2
Capital Expenditure $30.1 $5.5
Student Spending $42.2* $101.7
Total $232.2 $301.4

Student Spending

The key methodological difference between the inputs used in the two studies relates to how the direct student spending values were analyzed. The student spending input in the 2018 study is over $53 million (109%) larger than the 2010 input when converted to 2018 dollars. There are several reasons for this increase despite a consistent number of students enrolled.

First, the 2010 study only accounted for spending from students residing outside the campus region, while the 2018 study includes all current students. This update in methodology is consistent with current best practices for single-campus studies and will allow the 2018 study to be comparable with other similar studies. If not for HSU, in-region students may leave the region to attend another university or find employment elsewhere, thus it is appropriate in a region-specific study to include their impact. Due to this difference in methodology, the 2018 Study accounts for the spending of approximately 3,000 more students than the 2010 study. Table C3 below provides a comparison of the enrollment data informing the student spending inputs for each study.

Table C3: Student Enrollment Data Comparison

Data Inputs 2010 Study 2018 Study
Number of Students 7,800 7,774
Number of Students Residing in the Campus Region 3,030 N/A
Number of Student Residing Outside of the Campus Region 4,770127 N/A
Total Students 4,770 7,774

Not only does the total number of students accounted for differ across studies, but the living situation and per-student expenditures differs as well. The 2010 study estimated that of the 4,770 students from outside the campus region, approximately 34 percent of students lived on-campus and 64 percent of students lived off-campus. This distinction is important as living situation influences per student expenditure. For the 2018 study, ICF received an estimate of number of students and per student cost associated with living on-campus, off-campus, and with their parents from the HSU Residence Life Office. The 2018 per-student spending data provided by HSU results in a per-student spend that is approximately $2,500 higher for on-campus students and $2,200 higher for off-campus students compared to the 2010 study. The difference in number of students by living situation and average spending per student contributes to the large variation in direct off-campus student spending, (a difference of close to $60 million). The 2018 methodology provides a more accurate estimate of the economic activity attributable to student spending as it relies on current housing and financial data from HSU.

Table C4 provides a breakdown of this comparison.

Table C4: Housing and Student Spending Estimates (Portion of Spending Included in the Study)

Data Inputs 2010 Study128 2018 Study
Number of Students in On-Campus Housing 1,612 2,049
Number of Students in Off-Campus Housing 3,158 5,635
Number of Students Living with Parents N/A 90
Average Annual per On-Campus Student Spend $3,180 ($3.654*) $6,167
Average Annual per Off-Campus Student Spend $11,730 ($13,479*) $15,673
Average Annual Spend per Student Living with Parents N/A $7,973
Total Student Spending $42.2 Million (48.5M*) $101.7 Million

To investigate what the current student spending results would look like if these methodological differences were accounted for, ICF first downscaled the total portion of the input value attributable to the per-student spending differences for on-campus and off-campus students mentioned above (approximately $17.8 million). ICF then discounted the student spending input sectors in the 2018 study using the percentage of “out-of-region” students from the 2010 study (61%). This approach decreases the total student spending input by approximately $50.5 million. The table below shows the side-by-side results when the student spending methodological differences are accounted for. Using this adjusted approach, the total industry activity impact is approximately $35 million lower than the 2018 analysis results.

Table C5: Hypothetical Impact Results, excluding Alumni Impact

Input Category 2010 Study*
($ Millions –
2018 Dollars)
2018 Study –
Adjusted Inputs
($ Millions)
2018 Study –
Original Inputs
($ Millions)
Industry Activity $218 $325.6 $360.3
Employment 2,280 4,250 4,590
State and Local Taxes $12.2 $19.3 $22.1

The additional discrepancy between the total economic impact estimated in the 2010 study and the adjusted 2018 study is attributed to the direct effect leakage factors in each of the models used. In economic modeling terminology, leakage is described as the portion of the spending that “leaks” out to other regions in exchange for goods that are not produced locally. In comparing the direct effect leakage factors for the two models, we see that the factor in the 2010 study is approximately 30 percent higher than in the 2018 study. The 2018 study relies on underlying model data from 2017, while the 2010 study relied on underlying model data from 2008. Because of the lower leakage factor in the 2018 study, 30 percent more of the initial spending is staying in the study region before being applied to the secondary impact multipliers, which compounds the difference in the overall effect, resulting in an overall higher impact of over $100 million. Because the employment impact can not be adjusted by inflation, the employment impact differential appears even more significant. Economies typically become more robust over time. As economies develop, a higher percentage of goods and services are able to be sourced locally and thus the impact on the region is more significant, both in terms of industry activity as well as employment.

University Operating & Auxiliary Spending

While less significant than the effects of the student spending methodology, other input vectors also differed across the two studies. The direct University and Auxiliary expenditures increased by $34.1 million in the 2018 study, or approximately 21%. This increase is in line with the University’s overall growth. Since 2013-14, the University’s operating budget has grown at an average annual rate of 4 percent.126

Capital Spending

Capital spending is highly variable year to year and thus the estimates used in ICF’s analyses are 4-year averages. While the 2018 study figure is much lower than the estimate used in the 2010 study, HSU expects capital expenditure to increase over the coming years. Notably, the university expects to spend an additional $33 million on capital projects over the course of the 2018 – 2019 academic year.

Alumni Impact

The 2010 methodology for estimating state-based alumni uses an average annual cumulative chance of an HSU graduate leaving the state, opposed to actual campus-specific data used in the 2018 study. The difference between methodologies resulted in 4,421 fewer alumni in the 2018 analysis, however the 2018 methodology provides a more precise estimate.