# Performance Budget Planning: The Case of a Research University

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## Abstract

We describe a performance budget planning model developed for a research university, comprised of a set of 88 key variables and 38 non-linear structural equations that describe interactions among them. These equations, based on the knowledge of research university’s financial working and theoretical considerations, relate expenditures and revenues to teaching and research operations. We demonstrate the value of this model for developing insight into the financial structure of the university. In particular, we show how the model aids in (1) comparing the effect of various policy alternatives on the performance of the university, (2) performing comparative statics analysis of any subset of variables of interest, (3) choice of policy variables and policy alternatives, and (4) gaining insight into the structure of the interactions for a given policy alternative in terms of the causal chain between policy variables and outcome variables. We also describe a computer implementation of the model and discuss a class of mathematical tools for policy planning analysis that facilitate the use and manipulation of models based on sets of nonlinear constraints.

## Keywords

Performance budget planning Financial models Causal ordering Decision support Applications## Notes

### Acknowledgements

The foundations of this paper were laid by Herb Simon. His are all the equations in the model as well as the idea of building strategic planning systems based on structural equations and capable of making explicit the causal ordering that they imply. Herb’s untimely death in 2001 has prevented us from publishing this paper jointly. We would like to thank Dr. Velupillai Kumaraswamy and an anonymous reviewer for suggestions that led to revising of the paper. Dave Zubrow, Jeff Bolton, Kevin Lamb, Felicia Ferko and Igor Reshetar from Carnegie Mellon University’s Office for Planning and Budget helped us with the development of PBM. Hong Shi, a graduate student in Information Science, University of Pittsburgh, helped with the programming end of an initial version of the system. The graphical user interface to PBM has been implemented in GeNIe, a graphical modeling environment originally developed at the Decision Systems Laboratory, University of Pittsburgh, available at http://www.bayesfusion.com/. We were supported in this work by a special Grant from the University Administration at Carnegie Mellon University. The first author was additionally supported by the National Science Foundation under Faculty Early Career Development (CAREER) Program, Grant IRI–9624629 and by University of Pittsburgh Central Research Development Fund and, more recently, by the National Institute of Health under Grants U01HL101066-01 and 1R01HL134673-01 and Department of Defence under Grant Number W81XWH-17-1-0556.

## References

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