Postsecondary Enrollment Demand-Gap Estimation Model: Projecting the Postsecondary Demand-Gap for California by 2035
The postsecondary education system in California has been under the pressure of expansion by the means of opening a new campus for several years. The Legislative Analyst’s Office has been recommending whether to open new campus or not as a part of its mandate. In 1990, it recommended opening up of one campus of CSU, however, its projections grossly underestimated the future demand. In 2017, the LAO in its report recommended that given the demand projections there is no need for a new campus. The study commissioned by the Chancellor’s office of California State University also made the same recommendation. The problem of underestimation of future projections can be detrimental for the economy of California as a lot of studies and white papers hint toward a huge skilled labor gap in California’s labor market. Moreover, these future projections are based on data sourced from Department of Finance’s projections, which is perhaps not considering anticipated success of the policy initiated toward improving student outcomes in the TK – 12 ecosystem in California. Through this study, I present the first version of a system dynamics model for the estimation of California’s postsecondary enrollment demand . The model has policy handles to simulate projections to measure successes in policy initiatives focused on improving TK – 12 student outcomes. The model helps make appropriate forecasts taking into consideration the outcomes of policy implementation being undertaken by the California Department of Education for improvement of student outcomes. These outcomes were observable in the increased demand for the postsecondary seats in the California’s public education system and were also being highlighted by the think tanks and policy analysts but were left unrecognized due to lack of methodology to estimate impact of such outcomes on the postsecondary enrollment demand. With my model it has been possible to estimate impact of such policy measures by undertaking sensitivity analysis of policy measures. The model not only proves to be a better method to carryout intangible measurements of policy outcomes for policy analysis and policy evaluation, but also to undertake sensitivity analysis on several policy measures.
Computational Analytics, Higher Education, Policy Analysis, Sensitivity Analysis, Simulation, System Dynamics, Economics, Political Science
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