Oz Politics

Introduction

The way of thinking that currently underpins the design and delivery of public sector services in Australia is the approach known as program logic. Program logic combines three conceptual elements into a cohesive tool for public sector management:

• systems theory;
• scientific method applied to the policy cycle; and
• program, organisational and fiscal congruence.

At the Federal level, program logic was first introduced with then Labor government’s Financial Management Improvement Programme (FMIP) in the mid 1980s. According to Mascarenhas (1996:221), “the thrust of the Financial Management Improvement Programme was to focus on goals and objectives (corporate plan), better management of resources (program management, organisational design) and performance evaluation”.

The current Coalition government has continued the program logic approach through the introduction of their accrual budgeting framework. In terms of program logic, the only change has been the move from a hierarchical outcomes framework to a hierarchical outcomes/outputs framework for achieving program, organisational and fiscal congruence. While the Coalition government has packaged the introduction of accrual budgeting as a substantial reform, it is only a minor refinement to the program logic approach. For reasons of simplicity, this page discusses the congruence principle in terms of Labor’s FMIP framework.

Systems theory

Inputs Processes Outputs Initial Outcomes Intermediate Outcomes Ultimate Outcomes

Program logic has its first foundation in general systems theory. From systems theory it takes the idea that policy interventions can be modelled as an open system. In the language of this model, governments direct inputs at specific process in order to produce outputs that will lead to desired outcomes in the client population or in the society as a whole. Within the model, these terms - inputs, processes, outputs and outcomes - have quite specific meanings:

• inputs include money, staff, information, skills, expertise, and physical facilities;
• processes are what is done - the tasks, activities and strategies;
• outputs are the actual goods, products and services produced; and
• outcomes are the differences in a consumer’s life or the changes to a society as a result of outputs.

A critical concept in program logic is the notion of a hierarchy of outcomes. Ultimate outcomes are the more significant, longer-term changes that are sought. They are usually expressed as changes in collective client status or as changes to society. An ultimate outcome in the health arena is increased life expectancy. In some program logic schemes, ultimate outcomes are called [societal or population] impacts.

Initial and intermediate outcomes are more immediate. They are more closely related to specific policy interventions. If we take as an example an information campaign on smoking, the outputs might include television advertisements, information kits, and a 1800 telephone advice number. The initial outcome to which these outputs contribute is an increased awareness in the general population about the dangers of smoking. The intermediate outcome is a reduction in the number of people smoking. Over time, these lower order outcomes would contribute to the ultimate outcome of increased life expectancy for the entire population.

Implicit in program logic is a causal model of the policy process, from inputs to ultimate outcomes. This causal model can be thought of as a hypothesis. It is the belief that if the policy maker does a particular thing it should achieve a desired change in the wider population.

Scientific method applied to the policy cycle

The second foundation of program logic is the application of scientific method to the policy cycle. The notion of a policy cycle comes from what Colebatch (1998:55) calls the ‘common-sense’ view of policy as the pursuit of goals through a logical succession of stages. In this view, the policy process is imagined as an endless cycle of: policy decisions; implementation; and performance assessment.

At the core of scientific method is the idea of developing and testing hypotheses in order to find the best solution to a problem. Under program logic, policy-makers develop a hypothesis about the best way to achieve an objective (the causal model from inputs to ultimate outcomes noted above). They then test their hypothesis (that is to say, they implement their policies and analyse the impacts of their policies). Considering their analysis, policy-makers can come to a conclusion about whether their policies work, and if they do, whether they are effective.

Performance assessment is the critical connection between scientific method and the policy cycle. It enables policy-makers to consider the extent to which they are achieving their objectives, and to act accordingly. Using performance information, policy-makers can then modify the mix of inputs and processes they select in order to better achieve their desired outcomes. Performance information is also used to minimise cost (and hence maximise efficiency).

We shall consider each step in the policy cycle in more detail.

• Problem identification. policy-makers begin with the desire to solve a particular problem. For example, in the realm of social policy, typical policy problems include disease, premature death, poverty, homelessness, unemployment, substance abuse, and caring for older people and people with disabilities.
• Goal setting. Once agreement is reached on the problems that are deserving of a policy intervention, the next step in the process of program logic is to articulate desired outcomes. The desired outcomes are statements of what policy-makers hope to achieve. They are expressed as program objectives.
• Policy instrument selection. Once a problem and its associated desired outcomes have been identified, policy-makers turn their focus to choosing the right mix of policy interventions. The desired outcomes are used as selection criteria to rank and select the policy interventions most likely to solve the identified problem. As noted above, this selection process assumes a ‘causal model’; the delivery of particular products, goods and services (outputs) will result in the desired changes in the client population and/or the society (outcomes).

In the language of systems theory, policy interventions consist of systems of inputs, processes and outputs. Moving beyond the language of systems theory for moment, policy interventions are closely linked with the idea of policy instruments. Considine (1994:41-45) identified four types of policy instrument: rule making, direct provision, grants, and taxes and charges. Howlett and Ramesh (1995:82) provide a larger taxonomy with instruments ranked according to their level of state involvement: family and community; voluntary organisations; private markets; information and exhortation; subsidies; auction of property rights; tax and user charges; regulation; public enterprises; and direct provision.

• Implementation. Having selected a set of policy interventions with the goal of achieving specific objectives in order to solve an identified problem, the selected policy interventions are implemented.
• Performance assessment. The last step in the policy cycle is performance assessment. There are two approaches to performance assessment: ongoing performance measurement and evaluation.

  An ongoing performance measurement approach identifies and collects performance information using performance indicators.

  

  There are two key types of comparative performance indicators: efficiency and effectiveness. Efficiency indicators show the extent to which program inputs are minimised for a given level of program output, and to which outputs are maximised for a given level of inputs. Typically, efficiency is measured and compared on the basis of the output unit cost. Effectiveness indicators show the extent to which actual outcomes match desired outcomes (that is to say, program objectives). Because access, equity and service quality are typical program objectives, these matters are often addressed through effectiveness indicators.

  In some cases, performance indicators use direct measures of the outputs produced and the outcomes achieved. More often, performance indicators (and especially outcome indicators) use indirect measures. The usual reason for selecting indirect performance indicators is the cost and complexity of direct information collection. For example, an indirect performance indicator of a reduction in the number of people smoking is per capita tobacco sales. This information is readily available to governments through their tobacco excise and taxation collection systems, whereas direct information on tobacco consumption requires governments to commission regular statistical surveys.

  The weakness with relying on performance indicators alone is that they do not challenge the underlying causal model. Performance indicators cannot measure causality or attribution (just because an outcome indicator moved in a particular direction does not mean it was as the result of a particular policy intervention). Outcome indicators are especially unreliable measures of program performance where the relationship between outputs and outcomes has not been empirically verified. Outcome indicators are also unreliable indicators of a specific program’s success where the desired outcomes are typically influenced by exogenous factors and/or the outputs of multiple programs.

  For this reason, in depth program evaluations complement ongoing performance measurement. Evaluation affords the opportunity to examine the underlying causal model. Program evaluation should explore:

  • the original identification of the problem;
  • whether the stated program objectives are appropriate for addressing that problem; and
  • the empirical evidence base for a particular policy intervention (and the unselected alternatives), especially in respect of the causal relationship between outputs and outcomes;

Program, organisational and fiscal congruence

The third foundation of program logic is the normative requirement for congruence between program structures, organisational structures and budget appropriations. Under the program logic approach, government resources and activities are organised into a formal hierarchical framework based on the long-term policy objectives to which they contribute. All of the staff and facilities are consequently focused on working towards achieving desired solutions to specific policy problems.

Congruence between the program structure and organisational structure is achieved by aligning the program structure of portfolios, programs and subprograms with the organisational structure of departments, divisions and branches. Each portfolio/department is then made responsible for the achievement of a set of ultimate outcomes. Within departments, each program/division is made responsible for the achievement of specific intermediate outcomes. And within divisions, each subprogram/branch is made responsible for the achievement of specific initial outcomes.

Program or performance budgeting is the mechanism used to achieve fiscal congruence. Each program/division or subprogram/branch is given a one-line budget allocation. Because of the congruence requirement, governments effectively appropriate money for the achievement desired outcomes expressed as program or supprogram objectives. This is contrasted with the system where governments appropriate money for inputs and processes (also known as line-item or input budgeting).

The theoretical benefit of this three-way congruence is that line managers have the flexibility to allocate their resources to the mix of inputs and processes that they determine are best suited to achieving the agreed outcomes. Whereas governments are responsible for deciding what will be achieved, departmental managers have (at least some) flexibility in determining how it will be achieved.

As noted in the introduction, the Coalition government has made a slight modification to the congruence principle. Government departments now have more flexibility in the approach they take to aligning their program structure with their organisational structure. In general, the result has been that organisational branches are responsible for delivering groups of identified outputs rather than achieving specific lower order outcomes. In practice this is a very minor change to the previous arrangements, primarily because of the very close relationship between outputs and initial outcomes. Some complexity is added in that government departments must identify both the outputs they produce from their own funds and those they purchase or fund on behalf of the government through administered moneys.

Comments

Program logic is model. Like all theories and models, the program logic model simplifies reality for the sake of producing understanding. While the model makes it easier to plan, implement and evaluate policy, at times this simplicity bites back. Like all models, if program logic is used uncritically, it can exert tyrannical control. However, when the limitations are recognised, program logic is a useful tool in public sector management. Some of the limitations with program logic include:

• Subjectivity in problem identification. The ways in which the problem and the desired outcomes are articulated will favour particular policy interventions. This especially the case with the formulation of initial and intermediate outcomes. Kellow (1990:99) argues the way in which one conceptualises and describes a social problem prescribes what they would consider an appropriate policy response. Colebatch (1993:44) goes further, questioning the linear relationship from problems to solutions, “Problems are formulated by people who can envisage a solution”.
• Political imperatives. Objectives are often framed to secure political support for a program. Colebatch (1998:69) notes that not all reasons are equal when it comes to supporting a policy. He observes that a school program that ‘improves the driving skills of younger people and saves lives’ commands more than a program that ‘gets the kids out of our hair while we mark their exams’ or ‘prepares the next generation of car buyers’.
• Political sensitivities. Government programs which deal with issues of significant political sensitivity tend to avoid establishing program objectives and performance indicators in respect of those sensitivities. For example, containing expenditure growth is usually a critical government objective for all health services. However, this objective can be depicted in the media as penny-pinching and under-resourcing the health system; at worst it can be pilloried for resulting in unnecessary deaths because more funds were not allocated. Consequently, fiscal objectives are usually absent from the stated objectives for a health program. If they do appear, it will be under code phrases such as “a sustainable health system”. In public policy it is not unusual for there to be some dissonance between the real and espoused objectives for a program.
• Complexity and heterogeneity. Stakeholders (governments, service providers, service users and the wider community) usually have different views about what a program should be trying to achieve. It is unusual for program objectives to encompass the desired outcomes of all stakeholders. Stakeholders also have different views about which aspect of performance should be given priority in performance assessment process. In setting program objectives, policy-makers will either accommodate this complexity and heterogeneity resulting in multiple conflicting goals and problematic implementation, or they will favour the interests of some stakeholders over others. Complexity and heterogeneity also mean that all performance indicators are of necessity reductionist. The challenge is to ensure that they do not excessively simplify reality and that they are not used in isolation to assess performance.
• Absence of an evidence base. In spite of the language of objective rationalism, it should be noted that policy-makers often lack the empirical evidence to support the assumed relationship between outputs and ultimate outcomes. In many areas of social policy the absence of compelling empirical information on the relationship between outputs and outcomes means that ideological fashion rather than something more rigorous often guides the selection of policy interventions.

Sources

Australian National Audit Office and Department of Finance (1996), Performance Information Principles: Better Practice Guide, AGPS, Canberra, [http://www.anao.gov.au/bpg_perf/perfinfo.pdf].

Department of Finance (1994), Doing Evaluations: A practical guide, AGPS, Canberra.

Department of Finance and Administration (1998), Specifying Outcomes and Outputs: Implementing the Commonwealth’s Accrual-based Outcomes and Outputs Framework, AGPS, Canberra, [http://www.dofa.gov.au/budgetgroup/policies/guidance and manuals/outcomes and outputs/OandO_Guidance_for_Review_26_Nov.doc]

Department of Health and Family Services (1997), National Leadership through Performance Assessment, Department of Health and Family Services, Canberra, [http://www.health.gov.au:80/pubs/hfsocc/hfsocc1a.htm].

H K Colebatch (1993), ‘Policy-making and Volatility: What is the Problem’, in Andrew Hede and Scott Prasser (editors) (1993), Policy-making in Volatile Times, Hale and Iremonger, Sydney, , p44.

H K Colebatch (1998), Policy, Open University Press, Buckingham.

Mark Considine (1994), Public Policy: A Critical Approach, Macmillan, Melbourne.

Michael Howlett and M Ramesh (1995), Studying Public Policy: Policy Cycles and Policy Subsystems, Oxford University Press, Toronto.

Owen E Hughes (1994), Public Management and Administration: An introduction, Macmillan, Melbourne.

Aynsley Kellow (1990), ‘Policy analysis concepts and health policy’, in Michael Muetzelfeldt (editor) (1990), Health Policy and Administration: Study Guide, Deakin University, Geelong.

R C Mascarenhas (1996), Government and the Economy in Australia and New Zealand: The politics of Economic Policy Making, Austin and Winfield, Bethesda.

Steering Committee for the Review of Commonwealth/State Service Provision (2000), Report on Government Services 2000, AGPS, Canberra, [http://www.pc.gov.au/service/gsp/2000/index.html].

Sue Funnell (1997), ‘Program Logic: An Adaptable Tool for Designing and Evaluating Programs’, Evaluation News and Comment, Vol.6, No.1, July 1997, pp. 5-7.