Public Management and Administration - Owen E.hughes

118 Public Management and Administration
(Putt and Springer, 1989, p. 24). These scientific skills are not independent but
rather interrelated; they are also related to what they call ‘facilitative skills’
(1989, p. 25) such as policy, planning and managerial skills.
So, while empirical skills are needed, there are other less tangible ones
needed as well. Both sets of skills point to the emphasis on training found in
policy analysis. If analysts inside the bureaucracy can be trained in scientific
skills and facilitative skills, the making of policy and its outcomes should be
improved.
Some of the empirical methods used in policy analysis include: (i) benefit–cost
analysis (optimum choice among discrete alternatives without probabilities);
(ii) decision theory (optimum choice with contingent probabilities); (iii) optimumlevel
analysis (finding an optimum policy where doing too much or too little
is undesirable); (iv) allocation theory (optimum-mix analysis) and (v) timeoptimization
models (decision-making systems designed to minimize time consumption)
(Nagel, 1990). In their section on options analysis – which they regard
as the heart of policy models – Hogwood and Gunn point to various operations
research and decision analysis techniques including: linear programming;
dynamic programming; pay-off matrix; decision trees; risk analysis; queuing theory
and inventory models. How to carry these out can be found in a good policy
analysis book. They are mentioned here for two reasons: first, to point out that
there are a variety of techniques and second, that they share an empirical approach
to policy.
As probably the key person involved in developing mathematical approaches
to policy issues, Nagel is naturally enthusiastic about their benefits, arguing
that policy evaluation based on management science methods ‘seems capable
of improving decision-making processes’ (Nagel, 1990, p. 433):
Decisions are then more likely to be arrived at that will maximize or at least increase societal
benefits minus costs. Those decision-making methods may be even more important
than worker motivation or technological innovation in productivity improvement. Hard
work means little if the wrong products are being produced in terms of societal benefits
and costs. Similarly, the right policies are needed to maximize technological innovation,
which is not likely to occur without an appropriate public policy environment.
One can admire the idea that societal improvement can result from empirical
decision-making methods. There are undoubtedly some areas in which these
techniques can be very useful, and, even in matters of complex policy, information
may be able to be acquired which it could not by normal means. For
example, monitoring or controlling road traffic is a governmental function
everywhere. Traffic studies have always been done at the relatively low level of
counting cars. When this is extended through decision analysis, by taking numbers
to a higher level, or building scenarios into computer-based models, it is
possible to predict traffic patterns in future, to decide where to place traffic
signals, or to use cost–benefit analysis to decide between two sites for a traffic
interchange. In this kind of example, empirical methods undoubtedly would