To move away from stealth in social programming, government must invest in the analytical tools that can account for the system’s complexity.

Ken Battle’s recent Policy Options article “Social Policy-Making Still Stealthy After All These Years” is a valuable reminder of the importance of his 1990 article (also in Policy Options) in which he described how large regressive changes were being made to a complex system of social benefits and taxes through nontransparent technical adjustments, particularly to indexation formulas. His concept of stealth captured in simple language the frustrations of many analysts and advocates who were, and still are, concerned about social programming cutbacks that were devised behind closed doors and lacked transparency, and that were thus difficult to criticize, let alone understand.

The recent article reminds us that the problem of stealth remains today, perhaps less in the area of indexation, but certainly in the lack of transparency that results from the complexity of social programming. He concludes that social advocates should remain vigilant.

A precondition to finding a solution is the recognition that stealthy policy-making can produce good results, given our existing program structures. The role of government is to allocate resources in ways that maximize gain and minimize pain in the short term, and that also move in a needed longer-term direction. Thus, the government’s priorities may call for a reduction in the total amount of funding devoted to programming, including social programming. Or they may call for a shift in spending priorities in social programming: for example, placing more emphasis on poverty reduction among children, on mental illness or on programs directed at First Nations people, and less on reasonably well-served areas, such as income support for seniors.

Happily, in countries like Canada that have strong economies and mature welfare state programming, reallocating spending priorities does not necessarily mean cutting the benefits of those who are already receiving them in areas that are not top priority. Inflation and real income growth over time mean that new areas can be allocated more funding while benefits in other areas remain constant or grow at a less rapid rate by, for example, not indexing them fully to inflation. That is, we can move in the needed direction without causing too much short-term pain. And, as has often happened in the past, governments can readily reintroduce full indexation or use other ways to increase payments to those whose benefits temporarily fall behind, when doing so becomes a priority.

In other words, in some cases the sensible way of making the spending reallocations needed to meet the priorities of the government of the day may well be to reduce the growth of expenditure in a program, often temporarily, by adjusting the escalation formula and making other adjustments. The real question is whether this can be done more openly, less stealthily, to allow critics and advocates to understand and debate options and priorities in a way that fosters consensus building.

Unhappily, complexity is deeply entrenched in most social programming. For example, in the retirement income system, the various levels of government have multiple programs with different objectives that interact with each other in ways that even experts find difficult to penetrate. Battle provides other examples of this complexity in his recent article. It is hard to see how we can significantly improve the openness and clarity of incremental reforms without reducing the labyrinthine complexity of the underlying system.

It is interesting to note that while most of the stealthy changes discussed by Battle have been regressive, much of the system’s complexity was introduced for progressive reasons. Examples are employment insurance and Old Age Security, which were designed to have multiple objectives and hence more beneficiaries and a larger base of public support. While having multiple objectives may therefore be useful in gaining political support for programming, it also results in complexity and lack of transparency. Stealth begets stealth.

What can be done? In the longer run, we can move in the direction of simplifying program structures and providing programs with clearer, measurable objectives. This may improve accountability, but it will not be quick or simple.

I laid out a framework for future directions in my IRPP Policy Horizons essay, The Enabling Society. To summarize, this would involve using analytical tools based on big data and predictive analytics that are now readily available in order to provide services and income supports tailored to individuals based on evidence of what has worked best in similar circumstances in the past. Doing this involves a shift away from the approach taken in most existing social programming, which mainly addresses the broadly defined needs of broadly defined groups of beneficiaries in a broadly uniform manner, at a single point in time. Over time, a more evidence-driven social programming will become more effective, transparent and accountable.

However, even in a mature system composed of separate individually focused programs with clearer, measurable objectives, the effects on the lives of individuals will typically be a result of the combined effects of many such programs, from different departments and orders of government, as well as from nongovernmental social interventions. The system as a whole will remain complex.

What we need above all is data and analytical tools that take account of the complexity of the system and its combined effects on individuals. With the right leadership, this is feasible and achievable in the short to medium term. In a paper titled Upgrading Social Policy Research and Advice, the Experts Panel on Income Security of the Council on Aging of Ottawa (of which I am a member) has proposed a way we could move in this direction. It involves greatly accelerating investment in developing administrative and longitudinal data from surveys and administrative sources, and in developing analytical tools such as microsimulation analysis that can increasingly shed light on the causal relationships between individuals with different needs and aspirations and their varying social circumstances, including the effects over time of a range of social programs. The same data and analytical tools are also required in order to move toward the simpler, evidence-driven programming mentioned above.

Battle urges social advocates to remain vigilant in fighting stealth. This of course involves continuing to fight for the short-term interests of those they represent. However, the goal of moving toward more transparent, evidence-based social programming cannot be easily accomplished on a program-by-program or interest-group-by-interest-group basis. Advocates may well consider the merits of creating new alliances to pressure policy-makers to work in these longer-term, cross-cutting directions.

Photo: Shutterstock/By Breaking The Walls


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