Beyond the ‘dashboard’ – understanding policy failure

Despite the emphasis on the evaluative role of non-profit board directors in most governance models (e.g. the EDM model), the use of models for evaluation of governance policies has been generally confined to use of output or outcome measures which can be monitored on a strategy ‘dashboard’.

The focus on ‘end product’ and ‘impact’ is of course a vital part of the director’s governance responsibility. These approaches help to answer questions of whether or how well a goal or objective was achieved. They may not however, allow improved understanding of the reasons for ‘governance policy failure’, offer ‘diagnostic insights’ as to whether that failure was a design or implementation problem, or indicate the extent to which both aspects may have been involved. Understanding the reasons for policy failure should ensure that future strategic planning and execution are carried out more effectively.

Many public policy analysis and evaluation models have been developed over the years, and there is a multitude of journal articles and texts dedicated to this specialised field. Some of these models assert that they can also be applied to other types of policy e.g. organisational policies.

Policy design Vs implementation

A model which struck me as being readily adaptable to non-profit policy governance was developed by Bob Hudson, David Hunter and Stephen Peckham from Kent University. Their paper, Policy failure and the policy-implementation gap: can policy support programs help? outlines four major types or causes of policy failure:

  • Overly optimistic expectations
  • Implementation in dispersed governance (which I take to mean involving complex responsibility and accountability structures)
  • Inadequate collaborative policy making
  • Vagaries of the policy cycle (while for public policy this relates primarily to electoral and budget cycles, it could just as easily be related to NFP directors’ terms of office or organisational revenue patterns, etc.)

Their model is presented schematically below (and in the header image above, alongside my policy typology*), outlining their analysis of the forms of support required to achieve improved policy design, monitoring, impact, and learning. Policy tracking measures include performance monitoring (which accommodates dashboard perspectives), but also problem solving and progress assessing measures. Implementation support measures are outlined in relation to managing and regulating, problem solving, and capacity building perspectives.

Most non-profit boards have developed a policy on policy making and review, but these governance policies tend to emphasise updating existing policies due to out-of-date compliance references, or the need to adjust delegation limits. From my observation, few adopt evaluation models or approaches which promote diagnostic insights into policy failure.

Non-profit boards and their policy / governance committees interested in enhancing their policy evaluation work may therefore find the paper by Hudson et al has much to offer them.

(*The Policy Cube is a companion illustration to the Strategic Cube, published in an earlier post).

Ambiguity and Conflict factors

Another public policy model which could assist non-profit boards when considering any mismatch between policy design and implementation outcomes, is Richard Matland’s Ambiguity-Conflict Matrix, illustrated below.

The four types or modes of policy implementation identified in this model are arranged in a matrix, reflecting the extent to which each is subject to low or high ambiguity and conflict. While framed around public policy structures and forces, these modes can also be adapted to non-profit settings. Factors such as ‘contextual conditions’, ‘resources’ and ‘power’ can all be readily applied, while ‘coalition strength’ can be reframed around stakeholder relations.

Where your policy or governance committee can assign one of these types of policy implementation to the policy under review, they may find it helpful to consider the influence of local ambiguity and conflict factors. As for the previous model therefore, reference to Richard Matland’s article about this model may be instructive.

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