3 Preface Lightcast is a leading provider of economic impact studies and labor market data to educational institutions, workforce planners, and regional developers in the U.S. and internationally. Since 2000, Lightcast has completed over 3,000 economic impact studies for educational institutions in three countries. Along the way, we have worked to continuously update and improve our methodologies to ensure that they conform to best practices. The present study reflects the latest version of our model, representing the most up-to-date theory for conducting human capital economic impact analyses. Among the most vital departures from Lightcast’s previous economic model is the conversion from traditional Leontief input-output multipliers to those generated by Lightcast’s Multi-Regional Social Accounting Matrix (MR-SAM). Though Leontief multipliers are based on sound theory, they are less comprehensive and adaptable than MR-SAM multipliers. Moving to the more robust MR-SAM framework allows us to increase the level of sectoral detail in the model and remove any aggregation error that may have occurred under the previous framework. This change in methodology primarily affects the regional economic impact analysis provided in Chapter 2. The multi-regional capacity of the MR-SAM also increases the accuracy with which we calculate the statewide labor and non-labor multipliers used in the investment analysis in Chapter 3. Another major change in the model is the replacement of John Parr’s development index with a proprietary mapping of instructional programs to regional industries. The Parr index was a significant move forward when we first applied it in 2000 to approximate the industries where students were most likely to find employment after leaving their institution. Now, by mapping the institution’s program completers to detailed regional industries, we can move from an approach based on assumptions to one based on the actual occupations for which students are trained. The new model also reflects changes to the calculation of the alternative education variable. This variable addresses the counterfactual scenario of what would have occurred if the institution did not exist. Those students that would have obtained a similar education elsewhere and worked in the region, regardless of the institution under analysis, are excluded from the impact. The previous model measured the distance between institutions and the associated differences in tuition prices to determine the change in the students’ demand for education. In the current model, we assume 15% of
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