Daily Archives: December 23, 2019

Policy Analysis in 750 Words: policy analysis for marginalized groups in racialized political systems

Note: this post forms one part of the Policy Analysis in 750 words series overview.

For me, this story begins with a tweet by Professor Jamila Michener, about a new essay by Dr Fabienne Doucet, ‘Centering the Margins: (Re)defining Useful Research Evidence Through Critical Perspectives’:

Research and policy analysis for marginalized groups

For Doucet (2019: 1), it begins by describing the William T. Grant Foundation’s focus on improving the ‘use of research evidence’ (URE), and the key questions that we should ask when improving URE:

  1. For what purposes do policymakers find evidence useful?

Examples include to: inform a definition of problems and solutions, foster practitioner learning, support an existing political position, or impose programmes backed by evidence (compare with How much impact can you expect from your analysis?).

  1.   Who decides what to use, and what is useful?

For example, usefulness could be defined by the researchers providing evidence, the policymakers using it, the stakeholders involved in coproduction, or the people affected by research and policy (compare with Bacchi, Stone and Who should be involved in the process of policy analysis?).

  1. How do critical theories inform these questions? (compare with T. Smith)

First, they remind us that so-called ‘rational’ policy processes have incorporated research evidence to help:

‘maintain power hierarchies and accept social inequity as a given. Indeed, research has been historically and contemporaneously (mis)used to justify a range of social harms from enslavement, colonial conquest, and genocide, to high-stakes testing, disproportionality in child welfare services, and “broken windows” policing’ (Doucet, 2019: 2)

Second, they help us redefine usefulness in relation to:

‘how well research evidence communicates the lived experiences of marginalized groups so that the understanding of the problem and its response is more likely to be impactful to the community in the ways the community itself would want’ (Doucet, 2019: 3)

In that context, potential responses include to:

  1. Recognise the ways in which research and policy combine to reproduce the subordination of social groups.
  • General mechanisms include: the reproduction of the assumptions, norms, and rules that produce a disproportionate impact on social groups (compare with Social Construction and Policy Design).
  • Specific mechanism include: judging marginalised groups harshly according to ‘Western, educated, industrialized, rich and democratic’ norms (‘WEIRD’)
  1. Reject the idea that scientific research can be seen as objective or neutral (and that researchers are beyond reproach for their role in subordination).
  2. Give proper recognition to ‘experiential knowledge’ and ‘transdiciplinary approaches’ to knowledge production, rather than privileging scientific knowledge.
  3. Commit to social justice, to help ‘eliminate oppressions and to emancipate and empower marginalized groups’, such as by disrupting ‘the policies and practices that disproportionately harm marginalized groups’ (2019: 5-7)
  4. Develop strategies to ‘center race’, ‘democratize’ research production, and ‘leverage’ transdisciplinary methods (including poetry, oral history and narrative, art, and discourse analysis – compare with Lorde) (2019: 10-22)

See also Doucet, F. (2021) ‘Identifying and Testing Strategies to Improve the Use of Antiracist Research Evidence through Critical Race Lenses

Policy analysis in a ‘racialized polity’

A key way to understand these processes is to use, and improve, policy theories to explain the dynamics and impacts of a racialized political system. For example, ‘policy feedback theory’ (PFT) draws on elements from historical institutionalism and SCPD to identify the rules, norms, and practices that reinforce subordination.

In particular, Michener’s (2019: 424) ‘Policy Feedback in a Racialized Polity’ develops a ‘racialized feedback framework (RFF)’ to help explain the ‘unrelenting force with which racism and White supremacy have pervaded social, economic, and political institutions in the United States’. Key mechanisms include (2019: 424-6):

  1. Channelling resources’, in which the rules, to distribute government resources, benefit some social groups and punish others.
  • Examples include: privileging White populations in social security schemes and the design/ provision of education, and punishing Black populations disproportionately in prisons (2019: 428-32).
  • These rules also influence the motivation of social groups to engage in politics to influence policy (some citizens are emboldened, others alienated).
  1. Generating interests’, in which ‘racial stratification’ is a key factor in the power of interest groups (and balance of power in them).
  2. Shaping interpretive schema’, in which race is a lens through which actors understand, interpret, and seek to solve policy problems.
  3. The ways in which centralization (making policy at the federal level) or decentralization influence policy design.
  • For example, the ‘historical record’ suggests that decentralization is more likely to ‘be a force of inequality than an incubator of power for people of color’ (2019: 433).

Insufficient attention to race and racism: what are the implications for policy analysis?

One potential consequence of this lack of attention to race, and the inequalities caused by racism in policy, is that we place too much faith in the vague idea of ‘pragmatic’ policy analysis.

Throughout the 750 words series, you will see me refer generally to the benefits of pragmatism:

In that context, pragmatism relates to the idea that policy analysis consists of ‘art and craft’, in which analysts assess what is politically feasible if taking a low-risk client-oriented approach.

In this context, pragmatism may be read as a euphemism for conservatism and status quo protection.

In other words, other posts in the series warn against too-high expectations for entrepreneurial and systems thinking approaches to major policy change, but they should not be read as an excuse to reject ambitious plans for much-needed changes to policy and policy analysis (compare with Meltzer and Schwartz, who engage with this dilemma in client-oriented advice).

Connections to blog themes

This post connects well to:

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Policy Analysis in 750 Words: entrepreneurial policy analysis

This post forms one part of the Policy Analysis in 750 words series overview and connects to ‘Three habits of successful policy entrepreneurs’.

The idea of a ‘policy entrepreneur’ is important to policy studies and policy analysis.

Let’s begin with its positive role in analysis, then use policy studies to help qualify its role within policymaking environments.

The take-home-messages are to

  1. recognise the value of entrepreneurship, and invest in relevant skills and strategies, but
  2. not overstate its spread or likely impact, and
  3. note the unequal access to political resources associated with entrepreneurs.

Box 11.3 UPP 2nd ed entrepreneurs

Entrepreneurship and policy analysis

Mintrom identifies the intersection between policy entrepreneurship and policy analysis, to highlight the benefits of ‘positive thinking’, creativity, deliberation, and leadership.

He expands on these ideas further in So you want to be a policy entrepreneur?:

Policy entrepreneurs are energetic actors who engage in collaborative efforts in and around government to promote policy innovations. Given the enormous challenges now facing humanity, the need is great for such actors to step forward and catalyze change processes” (Mintrom, 2019: 307).

Although many entrepreneurs seem to be exceptional people, Mintrom (2019: 308-20) identifies:

  1. Key attributes to compare
  • ‘ambition’, to invest resources for future reward
  • ‘social acuity’, to help anticipate how others are thinking
  • ‘credibility’, based on authority and a good track record
  • ‘sociability’, to empathise with others and form coalitions or networks
  • ‘tenacity’, to persevere during adversity
  1. The skills that can be learned
  • ‘strategic thinking’, to choose a goal and determine how to reach it
  • ‘team building’, to recognise that policy change is a collective effort, not the responsibility of heroic individuals (compare with Oxfam)
  • ‘collecting evidence’, and using it ‘strategically’ to frame a problem and support a solution
  • ‘making arguments’, using ‘tactical argumentation’ to ‘win others to their cause and build coalitions of supporters’ (2019: 313)
  • ‘engaging multiple audiences’, by tailoring arguments and evidence to their beliefs and interests
  • ‘negotiating’, such as by trading your support in this case for their support in another
  • ‘networking’, particularly when policymaking authority is spread across multiple venues.
  1. The strategies built on these attributes and skills.
  • ‘problem framing’, such as to tell a story of a crisis in need of urgent attention
  • ‘using and expanding networks’, to generate attention and support
  • ‘working with advocacy coalitions’, to mobilise a collection of actors who already share the same beliefs
  • ‘leading by example’, to signal commitment and allay fears about risk
  • ‘scaling up change processes’, using policy innovation in one area to inspire wider adoption.

p308 Mintrom for 750 words

Overall, entrepreneurship is ‘tough work’ requiring ‘courage’, but necessary for policy disruption, by: ‘those who desire to make a difference, who recognize the enormous challenges now facing humanity, and the need for individuals to step forward and catalyze change’ (2019: 320; compare with Luetjens).

Entrepreneurship and policy studies

  1. Most policy actors fail

It is common to relate entrepreneurship to stories of exceptional individuals and invite people to learn from their success. However, the logical conclusion is that success is exceptional and most policy actors will fail.

A focus on key skills takes us away from this reliance on exceptional actors, and ties in with other policy studies-informed advice on how to navigate policymaking environments (see ‘Three habits of successful policy entrepreneurs’, these ANZSOG talks, and box 6.3 below)

box 6.3

However, note the final sentence, which reminds us that it is possible to invest a huge amount of time and effort in entrepreneurial skills without any of that investment paying off.

  1. Even if entrepreneurs succeed, the explanation comes more from their environments than their individual skills

The other side of the entrepreneurship coin is the policymaking environment in which actors operate.

Policy studies of entrepreneurship (such as Kingdon on multiple streams) rely heavily on metaphors on evolution. Entrepreneurs are the actors most equipped to thrive within their environments (see Room).

However, Kingdon uses the additional metaphor of ‘surfers waiting for the big wave’, which suggests that their environments are far more important than them (at least when operating on a US federal scale – see Kingdon’s Multiple Streams Approach).

Entrepreneurs may be more influential at a more local scale, but the evidence of their success (independent of the conditions in which they operate) is not overwhelming. So, self-aware entrepreneurs know when to ‘surf the waves’ or try to move the sea.

  1. The social background of influential actors

Many studies of entrepreneurs highlight the stories of tenacious individuals with limited resources but the burning desire to make a difference.

The alternative story is that political resources are distributed profoundly unequally. Few people have the resources to:

  • run for elected office
  • attend elite Universities, or find other ways to develop the kinds of personal networks that often relate to social background
  • develop the credibility built on a track record in a position of authority (such as in government or science).
  • be in the position to invest resources now, to secure future gains, or
  • be in an influential position to exploit windows of opportunity.

Therefore, when focusing on entrepreneurial policy analysis, we should encourage the development of a suite of useful skills, but not expect equal access to that development or the same payoff from entrepreneurial action.

See also:

Compare these skills with the ones we might associate with ‘systems thinking

If you want to see me say these depressing things with a big grin:

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Policy Analysis in 750 Words: complex systems and systems thinking

This post forms one part of the Policy Analysis in 750 words series overview and connects to previous posts on complexity. The first 750 words tick along nicely, then there is a picture of a cat hanging in there baby to signal where it can all go wrong. I updated it (22.6.20) to add category 11 then again (30.9.20) when I realised that the former category 11 was a lot like 6.

There are a million-and-one ways to describe systems and systems thinking. These terms are incredibly useful, but also at risk of meaning everything and therefore nothing (compare with planning and consultation).

Let’s explore how the distinction between policy studies and policy analysis can help us clarify the meaning of ‘complex systems’ and ‘systems thinking’ in policymaking.

For example, how might we close a potentially large gap between these two stories?

  1. Systems thinking in policy analysis.
  • Avoid the unintended consequences of too-narrow definitions of problems and processes (systems thinking, not simplistic thinking).
  • If we engage in systems thinking effectively, we can understand systems well enough to control, manage, or influence them.
  1. The study of complex policymaking systems.
  • Policy emerges from complex systems in the absence of: (a) central government control and often (b) policymaker awareness.
  • We need to acknowledge these limitations properly, to accept our limitations, and avoid the mechanistic language of ‘policy levers’ which exaggerate human or government control.

See also: Systems science and systems thinking for public health: a systematic review of the field

Six meanings of complex systems in policy and policymaking

Let’s begin by trying to clarify many meanings of complex system and relate them to systems thinking storylines.

For example, you will encounter three different meanings of complex system in this series alone, and each meaning presents different implications for systems thinking:

  1. A complex policymaking system

Policy outcomes seem to ‘emerge’ from policymaking systems in the absence of central government control. As such, we should rely less on central government driven targets (in favour of local discretion to adapt to environments), encourage trial-and-error learning, and rethink the ways in which we think about government ‘failure’ (see, for example, Hallsworth on ‘system stewardship’, the OECD on ‘Systemic Thinking for Policy Making‘, and this thread)

  • Systems thinking is about learning and adapting to the limits to policymaker control.

  1. Complex policy problems

Dunn (2017:  73) describes the interdependent nature of problems:

Subjectively experienced problems – crime, poverty, unemployment, inflation, energy, pollution, health, security – cannot be decomposed into independent subsets without running the risk of producing an approximately right solution to the wrong problem. A key characteristic of systems of problems is that the whole is greater – that is, qualitatively different – than the simple sum of its parts” (contrast with Meltzer and Schwartz on creating a ‘boundary’ to make problems seem solveable).

  • Systems thinking is about addressing policy problems holistically.
  1. Complex policy mixes

What we call ‘policy’ is actually a collection of policy instruments. Their overall effect is ‘non-linear’, difficult to predict, and subject to emergent outcomes, rather than cumulative (compare with Lindblom’s hopes for incrementalist change).

This point is crucial to policy analysis: does it involve a rethink of all instruments, or merely add a new instrument to the pile?

  • Systems thinking is about anticipating the disproportionate effect of a new policy instrument.

These three meanings are joined by at least three more (from Munro and Cairney on energy systems):

  1. Socio-technical systems (Geels)

Used to explain the transition from unsustainable to sustainable energy systems.

  • Systems thinking is about identifying the role of new technologies, protected initially in a ‘niche’, and fostered by a supportive ‘social and political environment’.
  1. Socio-ecological systems (Ostrom)

Used to explain how and why policy actors might cooperate to manage finite resources.

  • Systems thinking is about identifying the conditions under which actors develop layers of rules to foster trust and cooperation.
  1. Performing the metaphor of systems

Governments often use the language of complex systems – rather loosely – to indicate an awareness of the interconnectedness of things. They often perform systems thinking to give the impression that they are thinking and acting differently, but without backing up their words with tangible changes to policy instruments.

  • Systems thinking is about projecting the sense that (a) policy and policymaking is complicated, but (b) governments can still look like they are in control.

Four more meanings of systems thinking

Now, let’s compare these storylines with a small sample of wider conceptions of systems thinking:

  1. The old way of establishing order from chaos

Based on the (now-diminished) faith in science and rational management techniques to control the natural world for human benefit (compare Hughes and Hughes on energy with Checkland on ‘hard’ v ‘soft’ systems approaches, then see What you need as an analyst versus policymaking reality and Radin on the old faith in rationalist governing systems).

  • Systems thinking was about the human ability to turn potential chaos into well-managed systems (such as ‘large technical systems’ to distribute energy)
  1. The new way of accepting complexity but seeking to make an impact

Based on the idea that we can identify ‘leverage points’, or the places that help us ‘intervene in a system’ (see Meadows then compare with Arnold and Wade).

  • Systems thinking is about the human ability to use a small shift in a system to produce profound changes in that system.
  1. A way to rethink cause-and-effect

Based on the idea that current research methods are too narrowly focused on linearity rather than the emergent properties of systems of behaviour (for example, Rutter et al on how to analyse the cumulative effect of public health interventions, and Greenhalgh on responding more effectively to pandemics).

  • Systems thinking is about rethinking the ways in which governments, funders, or professions conduct policy-relevant research on social behaviour.

  1. A way of thinking about ourselves

Embrace the limits to human cognition, and accept that all understandings of complex systems are limited.

  • Systems thinking is about developing the ‘wisdom’ and ‘humility’ to accept our limited knowledge of the world.

hang-in-there-baby

How can we clarify systems thinking and use it effectively in policy analysis?

Now, imagine you are in a room of self-styled systems thinkers, and that no-one has yet suggested a brief conversation to establish what you all mean by systems thinking. I reckon you can make a quick visual distinction by seeing who looks optimistic.

I’ll be the morose-looking guy sitting in the corner, waiting to complain about ambiguity, so you would probably be better off sitting next to Luke Craven who still ‘believes in the power of systems thinking’.

If you can imagine some amalgam of these pessimistic/ optimistic positions, perhaps the conversation would go like this:

  1. Reasons to expect some useful collaboration.

Some of these 10 discussions seem to complement each other. For example:

  • We can use 3 and 9 to reject one narrow idea of ‘evidence-based policymaking’, in which the focus is on (a) using experimental methods to establish cause and effect in relation to one policy instrument, without showing (b) the overall impact on policy and outcomes (e.g. compare FNP with more general ‘families’ policy).
  • 1-3 and 10 might be about the need for policy analysts to show humility when seeking to understand and influence complex policy problems, solutions, and policymaking systems.

In other words, you could define systems thinking in relation to the need to rethink the ways in which we understand – and try to address – policy problems. If so, you can stop here and move on to the next post. There is no benefit to completing this post.

  1. Reasons to expect the same old frustrating discussions based on no-one defining terms well enough (collectively) to collaborate effectively (beyond using the same buzzwords).

Although all of these approaches use the language of complex systems and systems thinking, note some profound differences:

Holding on versus letting go.

  • Some are about intervening to take control of systems or, at least, make a disproportionate difference from a small change.
  • Some are about accepting our inability to understand, far less manage, these systems.

Talking about different systems.

  • Some are about managing policymaking systems, and others about social systems (or systems of policy problems), without making a clear connection between both endeavours.

For example, if you use approach 9 to rethink societal cause-and-effect, are you then going to pretend that you can use approach 7 to do something about it? Or, will our group have a difficult discussion about the greater likelihood of 6 (metaphorical policymaking) in the context of 1 (the inability of governments to control the policymaking systems we need to solve the problems raised by 9).

In that context, the reason that I am sitting in the corner, looking so morose, is that too much collective effort goes into (a) restating, over and over and over again, the potential benefits of systems thinking, leaving almost no time for (b) clarifying systems thinking well enough to move on to these profound differences in thinking. Systems thinking has not even helped us solve these problems with systems thinking.

See also:

Why systems thinkers and data scientists should work together to solve social challenges

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