Abstract
This paper addresses questions about modeling the implementation requirements of a public policy proposal. Can modeling provide advance warning of problematic implementation requirements inherent in the design of a policy idea? Going further, can it suggest feasible redesign options to improve the chances for desired outcomes? Our methodology, system dynamics, is more than just a simulation tool; it also a method of scientific inquiry that fosters operational thinking about how to improve the functioning of complex social systems. Our model is motivated by a case often cited as the seminal work in the implementation literature: Pressman and Wildavsky’s narrative of problems that undercut a US policy to combat persistent unemployment among minorities in Oakland, California in the late 1960s.
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Notes
- 1.
The first version, “Public Policy Implementation Modeling: The Case of EDA in Oakland,” was presented at the International System Dynamics Society Conference in Boston in July 2015. A substantially revised version was presented at the IJPA Symposium at the University of Palermo in May 2016.
- 2.
A light illustration of responsibility avoidance is Will Rogers' facetious suggestion during World War I that the best way to fight enemy submarines was to boil the Atlantic Ocean. When asked how that might be done, he replied, “I’m a policy man. I let others worry about implementation” (cited in Wheat 2010).
- 3.
Bardach (1977) develops the concept of an implementation assembly process.
- 4.
The Pressman and Wildavsky book is the sole source of facts about the Oakland case, although their case study has generated analyses too numerous to count (e.g., a Google search for “Pressman and Wildavsky” yields 15,000 hits).
- 5.
As are the authors. One of us was literally present at the creation of the Oakland case study project led by Pressman and Wildavsky at Berkeley, having been a professor of public policy at the Goldman School of Public Policy since 1970. At that time, the other author was a student of public policy at Harvard’s Kennedy School, thereafter serving on the White House staff. We have seen our share of gaps between policy efforts and outcomes, not only in academic research but also while in government staff positions and as consultants to governments.
- 6.
In the model, LTU Employed refers to long-term unemployed persons actually hired, and that is the variable graphed in Fig. 2. However, we should emphasize that whatever the actual employment total in Oakland, only a fraction of that number included the target population, and this discrepancy is not specified in our model. In addition to assuming no training capacity, the simulation results in Fig. 2 also assume weak cooperation between World and EDA, the interpretation of which is explained in the text.
- 7.
For this test, a training program is activated so that the stock of qualified applicants is large enough to accommodate the desired hiring rate.
- 8.
The strength of loop R1, assuming it is activated, depends on assumptions about the reaction functions influencing the government and the company. For example, we assume the government increases the normal reimbursement time by 3% when LTU Employed is 10% below the government's target level (elasticity = −0.3). We assume the company slows the hiring adjustment to match the slowdown in the reimbursement process (elasticity = 1.0).
- 9.
The reimbursement loop R** aggregates two loops—one stemming from wages and the other from investment. However, R** never becomes a closed loop unless the C* loops are active, in which case Projects Funds would be zero. If R** raised Project Funds above zero, that would make the C* loops dormant and immediately deactivate R**. The Project Funds stock constrains spending on investment and wages but it does not drive those outflows. Similarly, the potential C** payroll loop has no effective feedback effect on LTU Employed because the loop is only closed when Project Funds is at or near zero. We include R** and C** in our total feedback loop count, but they could not be responsible for the model’s goal-seeking behavior.
- 10.
For example, AnyLogic (anylogic.com) software supports both agent-based and system dynamics modeling. Moreover, one of our colleagues at the University of Bergen, Pål Davidsen, is using features of Stella Architect (iseesystems.com) to represent individual agents interacting within a system dynamics model.
- 11.
The Oakland model is available for online simulation at https://sims.iseesystems.com/david-wheat/oakland/#page1. Readers wishing to use Stella Architect to study model equations and experiment with alternative formulations are encouraged to request a fully editable copy of the model from the authors.
References
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Wheat, I.D., Bardach, E. (2018). Disappointing Outcomes: Can Implementation Modeling Help?. In: Borgonovi, E., Anessi-Pessina, E., Bianchi, C. (eds) Outcome-Based Performance Management in the Public Sector. System Dynamics for Performance Management, vol 2. Springer, Cham. https://doi.org/10.1007/978-3-319-57018-1_10
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