industry) based upon benchmarks of consumer expenditures provided by the Bureau of Labor Statistics. Indirect and induced expenditures were derived from the model results.
STUDENT EXPENDITURES
Student spending was estimated in the following manner. First, the number of students for Fall 2003 was obtained from the UT System office. Students were classified as originating from within the host region of the institution or from outside of the region. While an argument can be made that had the institution not been located within the region, prospective students would have been forced to move elsewhere to attend school, we assumed that only the students who moved into the local region to attend school would have an impact on the local economy since they are bringing new money into the region to spend on local goods and services. By multiplying the number of out-of-region students by average expenditures, we were able to estimate the total expenditures spent within the region.
The financial aid office of each institution is required by the US Department of Education to estimate student costs based upon a nine-month school year. While these statistics give reasonable estimates of cost, there is a degree of inconsistency in how each campus derives its estimates, so this study balanced these with statistics on consumer expenditures provided by the Bureau of Labor Statistics.24
First, the average estimated expenditures for students at all UT System institutions were determined to be $10,568 per academic year ($1,174 per month), which represent only personal expenses above and beyond those paid to the university for tuition, fees, books, etc. These estimates were then compared to the consumer expenditure data for the lowest income individual for the southern region in the Bureau of Labor Statistics’ estimates. Spending in specific categories was adjusted up or down. For instance, expenditures for books were adjusted upward to reflect student costs while expenditures for education (tuition and fees) were changed to zero since these expenditures are already reflected in the operational expenditures of the University. Allocation of expenditures into specific expense categories were estimated based upon the proportional representation of expenditures in the Bureau of Labor Statistics’ data (after the aforementioned adjustments were made). These expenditure estimates are considered conservative not only because of the methods by which they are derived, but because of the fact that students may attend college year round and/or remain in the region during the summer, thus impacting the regional economy.25
By multiplying the number of out-of-region students by the estimated expenditures, we have a total estimated direct
expenditure of goods and services by students originating from outside the institution’s home region. This represents new money coming into the region.
These direct expenditures were then allocated to IMPLAN sectors for estimating the indirect and induced impacts of student expenditures. The resulting figures give an estimate of the impact of student spending on the regional economy from students originating from elsewhere. These expenditures represent new resources coming into the local economies.
18 Caffrey,John, Herbert H. Isaacs. Estimating the Impact of
a College or University on the Local Economy. Washington, D.C.: American Council on Education, 1971.
19 Studies that have used similar methodologies with the
IMPLAN Input/Output Model have included: Engines of Economic Growth: The Economic Impact of Boston’s Eight Research Universities on the Metropolitan Boston Area, Appleseed, 2003; The Economic Impact of the University of South Carolina System, Division of Research, The Darla Moore School of Business at the University of South Carolina, June 2000; The Economic Impact of University System of Georgia Institutions on their Regional Economies, March 2002, by Sharon Duhart of the Board of Regents Offi ce of Economic Development; and the Economic Impact of Tarleton State University by S. Hussain Ali Jafri, Jay Dudley and David Buland, May 9, 2000.
20 Minnesota IMPLAN Group, Inc., IMPLAN System (data
and software), 1725 Tower Drive West, Suite 140, Stillwater, MN 55082 www.implan.com. The 2002 Data was used for this analysis (the most current available).
21 Expenditures are detailed on schedule C-2 of these reports. 22 Local Purchase Coeffi cients estimate the amount of an
industry’s needs that can be supplied by local industries based upon the makeup of the regional economy. Higher local purchase coeffi cients indicate that more items are purchased from local suppliers.
23 Expenditures are detailed on schedule B-11 of these
reports.
24 Bureau of Labor Statistics, Consumer Expenditure data for
2000-2003.
25 Similar methods were used for economic impact studies
for the University of Georgia and University of Wisconsin Systems (The Economic Impact of University System of Georgia Institutions on Their Regional Economies, March 2002 by Sharon Duhart; and University of Wisconsin System’s Economic Contribution to Wisconsin by Dennis K. Winters and William A. Strang, Sept 2002).
Census data show a positive correlation between higher earnings and educational attainment. This report uses data compiled and reported in The Big Payoff: Educational Attainment
and Synthetic Estimates of Work-Life Earnings by Jennifer
Cheesman Day and Eric C. Newburger for the US Department of Commerce, Economics and Statistics Administration, US Census Bureau in July 2002.
They determined “synthetic” estimates of work-life earnings from the working population’s 1-year annual earnings and summing their age-specific average earnings for people ages 25 to 64 years.
The resulting totals represent what individuals with the same educational level could expect to earn, on average, in 1999 dollars (we used a CPI adjustment factor of 2.6 percent per year to adjust their figures to 2004), during a hypothetical 40-year working life.” They defined a typical work-life as the period from age 25 through age 64. The authors acknowledge that some people stop working at ages other than 64, or start working before age 25, but believe that this range of 40 years provides a reasonable benchmark for many people.
ASSUMPTIONSAND LIMITATIONS
As with any national-level survey and accompanying statistical analysis, The Big Payoff: Educational Attainment and Synthetic
Estimates of Work-Life Earnings uses a number of assumptions
and is subject to limitations. Assumptions
• The estimates assume current cross-sectional earnings are representative of the patterns in future earnings.
• The average earnings of individuals in each age group have been based on all members within an age group without regard to work history, past performance, or other factors which may affect pay.
• The estimates do not account for any future productivity gains in the economy, and therefore, the estimates may be low.
• Their report assumes uninterrupted labor force participation from age 25 to 64.
• The earnings are based on currently surviving workers. Past research indicates that due to a differential mortality by education, these work-life estimates may be inflated differentially by education level.
Limitations
• Selecting only resident, noninstitutional population with earnings excludes a segment of adults with less education. This results in a higher estimate of the earnings of people with less education, and consequently, may understate the difference in work-life earnings between workers with less education and workers with more.
• Their report does not cover many other factors which affect earnings:
o College major
o Continuity of occupation
o Motivation and effort of individuals
o Occupation o Gender o Marital status o Family responsibilities o Income requirements o Area of residence
o Local job markets
• Statistics from sample surveys are subject to sampling and non-sampling error.
An estimate of the UT System’s aggregate incremental earnings impact from its 34,897 degrees awarded in FY2004 alone would be $44.6 billion overall, and considering the 86 percent of graduates staying to work in Texas would be $38.4 billion (Table 26). In contrast to the total UT System State appropriations for FY2004 of $1.6 billion, this gain in productivity for the graduating class’s lifetime represents a return to Texas exceeding 24 to 1.
Figures are averages, and represent incremental earnings potential over and above a typical high-school degree recipient with a typical lifespan and career, which by comparison would earn $1,364,326 in 2004 dollars. As an example, a 2004 graduate with a master’s degree would earn $1,405,806 above the $1,364,326 that he or she would have earned with just a high school diploma. The total earnings over his or her forty-year work-life would be $2,770,132, or about $69,253 per year.
This analysis does not differentiate among specific degree fields of study or granting institutions. For example, future surveying of UT graduates could better determine differences between careers (Finance, English, Science) and among institutions.