Navigating Complexity in Policy Implementation
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It has become common to invoke the idea of complexity – understood as intricate and with multiple moving parts – in contemporary policy debates. What do we understand by complexity? What insights does complexity offer public managers? What are the different types of complexities that policy designers and public managers navigate? This chapter explores these questions focusing on the implementation stage of the policy process. The chapter argues that complexity in the implementation stage can be understood at three nested levels. At the macro level, it manifests in the constraints that the policy environment imposes on public managers; at the meso level, it relates to the implementation architecture and the different types of policy tools used; and at the micro level, it relates to calibrating or making changes to these tools. Two arguments are developed. The first is that a nested understanding of complexity can help inform the debate on the specific challenges public managers confront. Second, in the short to medium term, public managers are more likely to address the complexities at the meso and micro levels and will need capabilities to manage or work around those at the macro level.
KeywordsPolicy complexity Policy implementation Policy tools Policy capacity Public managers
It has become commonplace to describe anything that the government does as “complex.” Governments are said to face complex problems, navigate complex policy environments, are use complex tools to address these problems. Indeed, there is a large literature around the application of complexity theory to better understand social and political systems (Wellstead et al. 2015; Cairney 2012). While these studies apply systems thinking to understand policy dynamics, there is a tendency for complexity to be used to describe even routine tasks or difficult activities in policymaking (Peters 2018). This is particularly true in the implementation stage of the policy process where policy decisions are put into practice.
One of the main reasons for this is that despite advances in recent years, implementation studies are largely undertheorized (Howlett 2019a; Durlak and DuPre 2008; O’Toole 2004). The first generation of implementation studies downplayed any risks in implementation and assumed that once policy decisions were made by political masters, they would be easily rolled out in a largely sanitized environment staffed with motivated and expert civil servants. While the second wave of implementation studies recognized these pitfalls, they were largely focused on the relative efficacies of bottom-up versus top-down approaches to implementation (Barrett 2004). The next wave of implementation studies focused on modelling administrative behavior using game theory, principal-agent theory, and more recently instrument selection theory. The disparate and diffused theorization of the implementation stage (see Howlett 2019a for a review) has contributed to analysts describing its dynamics as “complex” or the “black box” of the policy process. While these terms perhaps convey the severity of the policy task, they offer limited insights to public managers or policy designers whose efforts are focused on solving a problem or generating value for citizens.
The goal of this chapter is not to speak to the theoretical debates in implementation research but to better understand the types of challenges that public managers face during policy implementation and importantly what can be done to ameliorate them. The rest of the chapter is organized as follows. The subsequent section canvasses the concept of complexity focusing on problems, environments, and tools. This is followed by a stylized discussion of complexity using the policy elements framework (Howlett and Cashore 2009). The concluding section focuses on the role of the public servant in navigating these different dimensions of complexity.
The arguments advanced in this chapter are anchored in two interrelated bodies of scholarship. The first is the “new” design orientation in the policy sciences, which focuses on understanding problems in their constituent elements and how these can be addressed through specific policy efforts (Howlett and Lejano 2013). The second is the recent scholarship in public management that has focused on studying policy successes and their determinants (Compton and ‘t Hart 2019; Luetjens et al. 2019). Both these efforts speak to improve our understanding of the challenges policy designers and public managers face and how best these can be addressed.
The Age of Complexity
Complexity in Policy Problems
In early 2017, the President of the United States, Donald J. Trump, echoing the frustration of governments across the world quipped “Nobody knew healthcare could be so complicated.” What is it about contemporary policy problems that make them complex? As Guy Peters reminds us, it is important to distinguish between the complicated and the complex. In the context of problems, complicated problems have multiple moving parts, but relationships among these variables and how they interact with each other are stable, well-known, and easy to predict (Peters 2018: 67). Complex problems, on the other hand, have multiple moving parts, but their dynamics and interactions are far less predictable or manageable. The idea of complexity in policy problems goes back to the notion of problems being “difficult,” “ill-structured,” “intractable,” and “wicked” (Head 2019; Peters 2017).
The attributes of complex problems
A complex system (problem) is greater than the sum of its parts, and the constituent elements of the problem are interdependent
Complex systems (problems) are shaped by their initial conditions and characterized by path dependence
Weak causal relationships
There are weak causal relationships among variables that influence and shape the problem. Further, the relationships among variables are not stable and predictable and may interact differently in heterodox contexts
There are multiple variables that are involved, and often only the symptoms, not the causes, are visible to the problem solver. The large number of variables means the problem solver must focus on only a subset and can, of course, choose incorrectly
The presence of multiple and possibly conflicting goals. To be successful in addressing a complex problem, a solution will have to satisfy multiple actors with different and probably conflicting goals
Changes in one variable can have multiple connections with other relevant variables, making predictions of consequences from even small changes difficult
Over- and underreaction
Given the uncertainties and endogeneity, it is difficult to establish appropriate responses to these problems. Policymakers typically “underreact” or “overreact”
The policymaking situation is prone to rapid and unpredictable changes, placing decision-makers under considerable pressure
Type 1 and 2 errors
As policymakers typically overreact or underreact, it gives rise to a series of errors in exclusion and inclusion in identifying beneficiaries or benefit levels
The timing of the effects of interactions is unpredictable and often delayed, and therefore designing becomes difficult
Addressing complex problems requires behavioral change among policy recipients, the magnitude and modalities of which remain unclear
Complex problems have “sleeper effects” through which the intended and/or unintended consequences of policy choices may be manifested at a future point in time
Types of uncertainty
Complexity in Policy Environments
Despite protracted public sector reforms over the past two decades that prioritized economic efficiency, contracting out, and the general “hollowing out” of state (Peters 2015), governments today are playing a much larger economic role than ever before (Tanzi 2011). The rise of plurilateral, collaborative, and “metagovernance” styles is fundamentally changing the extent to which, and how, governments interact with stakeholders in the provision of public goods and services (van Der Wal 2017). Public managers now have to navigate increasingly messy and blurred boundaries (Kettl 2016; O’Flynn 2019). Political and economic decentralization has also contributed to the growing complexity in the policy environment. For example, there are at least eleven government agencies and ministries (apart from the Ministry of Health) that are involved with health reform in China (Qian 2015). While decentralization reforms have focused on improving accountability and responsiveness, they have also placed administrative burdens within different levels of government in coordinating services. For example, in India, transferring funds from the Ministry of Health to State-level implementing agencies required “clearances” from 32 “desks” in Bihar, 25 in Maharashtra, and 10 in Odisha (Choudhury and Mohanty 2018). This in turn has increased the waiting time for releasing funds. In a particularly egregious case in Bihar in 2016–2017, the first instalment of funds reached the implementing agency only in the last quarter of the fiscal year.
The policy environment that public servants have to navigate today is also characterized by increasing uncertainty and ambiguity (van Der Wal 2017). Public managers have to rely on novel policy approaches to accommodate these dimensions of uncertainty (Table 2). But effectively using these requires sophisticated policy capabilities that extend beyond technical skills and key operational and political competencies (Hartley et al. 2015; Bali and Ramesh 2018). Amplifying these challenges are declining levels in trust in government and political leaders (Hetherington 2005). In Australia, for example, in 2018, only about two-fifths of the population expressed satisfaction in political leaders and democracy – down from 80% 15 years ago (Evans et al. 2018). The recent attacks on experts and expertise (Nichols 2017) and proliferation of agnotology (Perl et al. 2018) have become a defining hallmark of contemporary public management.
Layered with these are challenges of continual disruptions (van Der Wal 2017), increasing contestability and demand for positional goods (Turner 2012), and declining economic and fiscal space. For instance, in a large review of how governments dealt with contingent liabilities, Bova et al. (2016) find that between 1990 and 2014, the average fiscal cost of these liabilities was 6% of GDP. Moreover, we also know that governments routinely underestimate their liabilities and public debt (through forecast errors) (IMF 2016). The important implication of these financial management practices for public managers is that once the range of contingent liabilities and routine forecast errors are recognized, governments are more constrained in responding to challenges.
Complex Policy Instruments and Mixes
Another manifestation of complexity is in the types of policy instruments and tools that are deployed to address contemporary challenges. Policy tools or instruments are essentially techniques by which governmental authorities give effect to their goals. In most policy areas, governments have come to rely on multiple tools that are assembled carefully in policy mixes or policy bundles (Howlett et al. 2015). This usually stems from the gravity of the problem which requires multiple instruments to shape economic and/or human behaviors (Howlett 2019b).
For example, to manage traffic congestion, Singapore relies on congestion pricing, auction markets, and registration fees. These primary tools are supported by supplementary tools such as fiscal subsidies to public transport operators and regulations that govern how taxi and bus operators set fares (Wu and Ramesh 2014). Over time, newer instruments are added to these, resulting in a layered policy mix. Thus, for example, Singapore has introduced regulations to govern ridesharing providers such as Uber and Grab. It has also introduced regulations on automated vehicles being trialled. As newer tools are introduced, ensuring that these tools work well together and are coordinated and coherent is challenging (Bali and Ramesh 2018). Moreover, although tools such as nudges and big data offer insights, we have yet to arrive at “best practices” on how these tools are layered within existing policy mixes (Giest 2017).
Another source of complexity is missing prerequisites which impacts the efficacy of these tools. Take, for example, social insurance, a common tool used to organize social protection in many developed societies. A fundamental institutional requirement for social insurance is formal labor markets. Yet, we see that the Philippines and Vietnam and to some extent India, countries with an overwhelmingly informal labor market, have relied on social insurance to organize key social protection programs. This typically results in requiring additional tools to ensure that those in the informal labor market have access to social insurance programs.
Another example is from the recent reforms in Vietnam’s health system. Vietnam experimented with paying district hospitals on a capitation basis, rather than using fee for service (FFS). Results from a pilot study suggest that while capitation payments resulted in lower costs, hospital managers were found to have increased the volume of services provided to the uninsured who continue to pay out of pocket on an FFS basis (see Nguyen et al. 2017). Newer tools to monitor such moral hazard and ensuring these cohere with the existing reforms are another source of complexity.
Similarly, PhilHealth, the national social insurance agency in the Philippines, implemented case-based payments for the most common 23 diagnoses in 2011 and paid hospitals on the basis of average cost of treatment charged in the past. Case-based payments were subsequently extended to all hospitalization episodes in 2013. However, providers continued to charge patients on an FFS basis minus the amount they would receive from PhilHealth on a case rate for treating the patient (Brendenkamp et al. 2016). The larger point that these examples point to is that understanding the impact of new policy tools and managing the impact require a lot of design work adding to the complexity of the task.
As the discussion in this section highlights, there are multiple sources of complexity – understood as multiple interconnecting parts or elements that need to be addressed contemporaneously – in policy problems, policy environments, and policy mixes. The current global pandemic, COVID-19, is a fitting example of the multiple types of complexities that governments have to navigate. For example, policymakers do not have a shared understanding on the nature of the disease and its impact across the economy or how it affects different members in a society; the policy environment in which governments are responding to the crisis is extremely ambiguous and volatile; and this has required governments marshalling a spectrum of policy tools (regulations, fiscal incentives, public provision, etc.)
Understanding Policy Complexity: A Nested Approach
First: What norms guide policy implementation?
Second: What policy instruments and mixes are used?
Third: How are these instruments calibrated and deployed?
Types of complexity during policy implementation
What are the challenges related to the policy environment, institutional architecture, and policy style that give rise to complexity?
What are the challenges in developing and deploying effective policy mixes?
What challenges are present in calibrating policy mixes and instruments?
At the macro level, public managers have to navigate a range of challenges that stem from their policy environment. O’Toole and Meier (2017) describe these as political, environmental, and internal contexts of public sector organizations. Layered to this is the capacity context of public sector organizations which constrain public managers (Wu et al. 2015; Bali and Ramesh 2018; Hartley et al. 2015). Navigating the political context gives rise to a range of challenges. For example, implementing policies in federal systems such as Australia, Canada, and India requires greater coordination with autonomous or quasi-autonomous agencies across regional and local governments. While the presence of multiple veto points (actors whose agreement is required to materially alter the status quo) can serve as a system of checks and balances, it also increases the political legwork required by public servants in the policy process.
For instance, in fragmented and adversarial political systems, public managers often participate in the political tasks of building consensus for agency-level initiatives. In political systems which concentrate power, public managers may find it easier to focus their energies on the internal dynamics of their agency (O’Toole and Meier 2017). Navigating a complex authorizing environment (to use Moore’s terminology) with multiple veto players and points and dispersed sources of political power adds to the complexities that public managers face.
Similarly, the growing reliance on external providers in service delivery and the presence of multiple principal-agent relationships across routine policy tasks increase the coordinating role of public managers. For example, ensuring compliance with protocols and standards among a diffuse and distributed network of providers in the National Disability Insurance Scheme (NDIS) in Australia during times of crises such as the recent pandemic can prove to be challenging (Dickinson 2020).
The degree of centralization in policymaking and implementation adds to the challenges described above. This is especially true in countries with wide variations in policy settings that need to be accommodated during policy implementation. Take, for example, India’s recent health insurance program which aims to cover 500 million citizens. Ayushman Bharath does not take into account wide variations in demographic, epidemiological, and socioeconomic profiles across India in designing hospitalization benefits. Thus, the southern state of Kerala with a total fertility rate of 1.8 and Uttar Pradesh with TFR of 3.1 have similar benefit packages though their health needs are widely different.
The task environment (the types of organizations and stakeholders which public managers have to engage with) gives rise to a series of complexities (Osborn and Hunt 1974). Task environments which are heterogenous involving multiple types of organizations and can require greater efforts by public servants to manage “outwards” (O’Toole and Meier 2017). This in turn contributes to a spectrum of complexities that public managers need to navigate. While most public sector organizations endeavor to routinize activities, a pervasive style of path dependency can serve as a double-edged sword. Routinized practices and processes make it easier for public managers to respond to different types of policy problems. But these routines can encumber public managers to respond to policy turbulence, understood as exogenous shocks that are obscure and difficult to plan. A task environment that is increasingly characterized by turbulence and exogenous shocks increases the capabilities public managers need.
Another source of complexity in the task environment relates to the clarity of policy goals, or what O’Toole and Meier describe as internal context. It is not uncommon for public managers to navigate competing or often ambiguously defined goals. For example, the US government spends billions of dollars on subsidizing tobacco farmers and contemporaneously billions in anti-smoking campaigns (Peters 2015). The institutional architecture and policy styles that operates within a sector can constrain how public managers and policy designers respond. This largely refers to “a distinctive style which affects … policy decisions, i.e. they develop tradition and history which constrains and refines their actions and concerns.” Policy studies typically draw on “policy regimes” that include the following elements: a common set of policy ideas (policy paradigm), a long-lasting arrangement of policies that have accumulated over time (policy mix), a common or typical policymaking process (policy style), and a more or less fixed set of policy actors (policy subsystem or policy monopoly). Together, these elements combine to ensure policy outputs remain very much within a range of options compatible with the pillars of the regime. Public managers have to navigate these policy styles and regimes which at times can be very inelastic. For example, until very recently Singapore refused to consider any policy reforms that would involve greater risk pooling in financing healthcare services. This in turn restricted policy options to deal with an increasingly aging population (Asher and Bali 2013; Ramesh and Bali 2019).
The capacity context is understood as the competencies and capabilities of public managers, the agencies in which they operate, and the government as a whole (Wu et al. 2015) shape how public managers can respond to contemporary problems. This not only includes technical know-how but also operational strengths in budgeting, coordinating, and planning and a spectrum of political savvy and skills (Hartley et al. 2015). Public managers contribute to the capacity of their agencies but are also constrained by the capabilities of the agency and government. While capacity deficits in some areas can be addressed by corresponding strengths in other capabilities of the government, some deficits may be so severe or critical that they undermine the effectiveness of a policy initiative (Howlett and Ramesh 2016). For example, efforts to achieve universal health coverage in the Philippines and Vietnam have been stymied by the inability of the governments to enforce contracts signed with healthcare providers (Bali 2016).
At the meso level, the complexity stems from the different types of policy tools that public managers have to actively deploy and implement. Contemporary tools such as taxes, licenses and subsidies, or contracts are supported by an implementation architecture (Howlett et al. 2020). The implementation architecture is shaped by (i) socio-politico-economic conditions (including socioeconomic conditions present in the economy; the level of technology sophistication required; the legitimacy of governments; attitudes and resources of constituency groups; the level of support from legislators; and the quality of bureaucracy); (ii) problem-centered conditions (include the technical nature of the problem; diversity of the target groups; size of the target groups; and extent of behavioral change required); and (iii) policy-centered conditions (which include clear and consistent objectives; adequate causal theory; coordination within and among implementing agencies; adequate decision authorities for implementing agencies; personnel and financial resources). Each of these elements can give rise to a series of challenges and increase the complexity at the meso level. Engaging with each of these elements is beyond the scope of this chapter except to note that there is little that public managers can change about the first two conditions during the implementation stage.
Another source of complexity at the meso level stems from active rolling out of programs, especially of new initiatives. For example, it is not uncommon for new programs to duplicate the benefits of existing programs administered by a different government agency. Peters (2015) cites the example of Ghana, where residents until recently had up to seven national identity cards, significantly increasing administrative costs. India’s flagship health insurance reform, the RSBY, when introduced in 2008, was administered by the Ministry of Labour, which had limited mechanisms for consultation with the Ministry of Health, which supervised existing health programs (Bali and Ramesh 2015).
Similar challenges occur in distributive and redistributive programs. As Herd and Moynihan (2019) note, such programs always give rise to errors of exclusion and inclusion. These, respectively, refer to intended beneficiaries not receiving benefits and the case when the benefits are received by individuals who are not intended beneficiaries of a program. And efforts to reduce these errors typically result in excessive administrative burdens being placed often on citizens who are vulnerable. For example, as of mid-April 2020, the state of Florida had processed only 15% of the unemployment insurance claims it had received (Herd 2020). Part of this was attributed to limited capacity to process large claims in the aftermath of large-scale economic crisis caused by COVID-19 and in part to the design of the program which placed excessive administrative burdens in accessing unemployment insurance.
Analogous to the issue of administrative burdens is the complexity of implementing programs with a large number of beneficiaries. The scale and complexity of introducing public expenditure programs in countries like Australia, Singapore, and New Zealand and those in larger economies such as China, India, and Indonesia are different. For example, rolling out a social pension program for the elderly in a relatively young country like India in 2020 involves a beneficiary base of 80 million individuals (compared to 3 million in Australia). The challenge over here is that errors of inclusion and exclusion are amplified in larger programs. Moreover, rolling back or reversing policy decisions that affect such a large number of individuals is challenging.
Public managers have to navigate another set of challenges in addition to those at the macro and meso levels. These are in calibrating policy tools or making changes to its settings in response to policy pressures. The literature in public management and in the policy sciences have not recognized the complexity involved in calibrating policies (Capano and Howlett 2020). For example, determining benchmark tariffs for electricity markets and taxes and fees for solid waste management and making changes to health insurance premiums or the subsidies provided to public hospitals are inherently complex tasks. Not only do these need to accommodate market pressures, but also some slack, allowing room for adjustments as conditions change. The ability to alter and adapt policies on the fly – to improvise effectively – requires some level of redundancy in program resources and parameters but may involve significant administrative and other redundancies and expenses in the short term which may make them difficult to enact or implement. This can be effective and even efficient over the long term, for example, in the case of “automatic stabilizers” such as welfare payments or unemployment insurance payments in the event of an economic downturn, maintaining some level of spending and saving despite a general economic contraction and removing some funds from investment availability during boom times.
Poorly considered changes to policy settings can undermine policy coherence. Coherence in this context refers to the consistency of actions in addressing a given set of policy problems or target groups. It is not uncommon that parametric changes that “stretch,” “layer,” or “patch” existing programs can over time change the underlying incentives or erode coherence (Howlett and Rayner 2013). For example, allowing public hospitals to pay their staff bonuses (a simple change in the policy setting of hospital reimbursements), which was ultimately based on the volume and price of services the doctors prescribed, contributed to increased out-of-pocket spending on healthcare in Vietnam in the mid-2000s (Ramesh 2013).
Conclusion: Navigating Policy Complexity
The central aim of this chapter has been to understand the different types of complexity that public managers and designers face in the implementation stage of the policy process. Complexity as a theoretical concept does little to speak to the debates in contemporary public management and in public policy that are focused on delivering value to citizens and on addressing policy problems, respectively. To speak to practitioners and the recent theoretical debates in the “new” design orientation and the emerging discussion around positive public administration alluded to at the outset of this chapter, we need to understand the different dimensions or types of complexities and importantly how can public managers address them.
Policy complexities and policy capabilities
Navigating micro-level complexities – investing in analytical capabilities
Navigating meso-level complexities – investing in operational capabilities
Navigating macro-level complexities – investing in political capabilities
Knowledge of policy substance and necessary analytical techniques
Leadership; strategic management; negotiation and conflict resolution
Understanding of the needs and positions of different stakeholders; judgment of political feasibility
Public sector organization
Knowledge of tools used across a policy sector such as healthcare or education
Funding; staffing; levels of intra-agency and inter-agency coordination; information architecture; budgeting and human resource management systems
Politicians’ support for the agency; levels of interorganizational trust and communication
Institutions for knowledge generation, mobilization, and use
Rule of law; transparent adjudicative system
Public legitimacy and trust in government
The challenges at the meso levels around the implementation architecture can be addressed by investing in leadership training and skills in commissioning (Dickinson and Sullivan 2014). Similarly, the proliferation of external providers and contractors in contemporary service delivery requires strong adjudicative systems and high levels of trust in government – attributes that are exogenous to public managers. However, skills in developing contracts can help ensure that contentious issues in the implementation architecture around contingent liabilities and risk management can be addressed (O’Flynn 2019). Similarly, at the agency level, coordination mechanisms across multiple levels of governments can help reduce the complexities described earlier in large federal economies. For example, in 2013 India introduced a nationwide public financial management system to track the flow of funds to the point of service delivery.
The challenges around calibrating policy tools at the micro level require developing innate technical skills (such as in health administration or pension economics or in water policy) and anticipatory capabilities (Bali et al. 2019). That is, using specific techniques outlined in Table 2 such as social graph making, scenario planning, sensitivity testing, and multicriteria mapping (Stirling 2010). Domain-specific skills are especially relevant in the context of calibrating complex tools such as diagnostic related groups (DRGs) in health systems or in making changes to how pension funds are regulated. Such skills allow a systemic perspective and ensure overall policy coherence, as governments use multiple tools to finance healthcare and manage pension funds. The latter skills around anticipatory capabilities help ensure that there are sufficient “degrees of freedom” or leeway in current policy settings to respond to exogenous shocks such as the current pandemic.
The challenges at the micro and meso levels in implementation discussed in this chapter are more easy to manage in the medium term, and public managers (and governments) can develop capabilities to do these (Dickinson and Sullivan 2014; van Der Wal 2017; Hartely et al. 2015). The challenges at the macro level largely stem from constraints imposed by an invidious policy environment that public managers have little active control over. Navigating these challenges and complexities is more about political savvy and astuteness than technical know-how.
The introduction to this chapter echoed a popular refrain that implementation studies are undertheorized, which in turn has allowed it to be described in metaphors that don’t offer insights to practitioners. The nested discussion of complexity offered in this chapter can help advance the debate on challenges that policy designers and public managers face in the implementation stage of the policy process and how these are addressed in different contexts.
Many thanks to Kris Hartely, Helen Dickinson, and Mike Howlett for constructive comments and suggestion on this chapter.
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