This research paper delves into the intricate domain of policy science, focusing on the policy-making process itself. Existing theoretical frameworks in policy science often overlook essential nuances, particularly the role of political willingness causing non-linearity in the policy-making process. Three fundamental questions drive the research at hand. First, the research delves into the adequacy of existing policy frameworks in effectively elucidating the complexities of policymaking in the context of large democracies, with a particular focus on India. Second, it investigates the significant role of political willingness in shaping the trajectory of policymaking, particularly within democratic systems. Lastly, the research aims to construct a comprehensive framework capable of accommodating and explaining the non-linear dynamics introduced by the influence of political willingness throughout the policy-making process. Through an analysis of India's Data Protection Regulation formulation process, this paper attempts to map the existing gap and way forward. As an outcome of the case study analysis, the proposed framework introduces political willingness as a central element extending the policy formulation cycle and showcases its influence on agenda setting, policy formulation, adoption, implementation, and evaluation. This research contributes to the field of policy science by providing a comprehensive framework that accounts for the complexities of policymaking, especially in the Indian context. By acknowledging the pivotal role of political willingness, this framework seeks to bridge the gap in existing theoretical models, offering a more holistic understanding of policy formulation and analysis. This research lays the foundation for a paradigm shift in the study of policy science, addressing the challenges posed by evolving governance mechanisms and the rising influence of political agendas in the modern world.
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Policy science is a vast academic discipline. From time to time, various academicians have proposed theoretical frameworks for academic and practitioner insight and use. At the same time, Policy formulation and analysis are two completely different aspects of Policy Science. Indeed, the analysis frameworks have been used for quite a long to train policymakers to ensure they do not repeat the mistakes made in the past.
The central impetus behind this research stems from the observation that while separate theories exist on how to make policies and how to analyse policies, a comprehensive theoretical framework that critically assesses the policy formulation process itself while accounting for the evolving roles of technology and governance mechanisms especially in large democracies like India is notably absent.
This issue first came to my notice when I tried to analyse the Data Protection Policy formulation Process in India from the lens of conventional policy frameworks, which the readers will find in the analysis section. The process of forming data protection regulations in India deviated from traditional norms. The journey also encountered multiple hiccups and underscored the role of political willingness. The eventual passage of the 2023 bill demonstrated the responsiveness of this unique policy formation journey, diverging from conventional approaches. In the paper, I have discussed the peculiarity of this policy formulation process and how and why it did not fit into the traditional frameworks.
There is a plethora of frameworks, some old and some new. I tried to understand this development from the lens of various theoretical frameworks, but most of them could not explain it. Describing how most frameworks could not explain the process is beyond the scope of one research paper. But, to initiate the discourse, in this paper, I have explained my quest through the lens of a few critical frameworks. The Policy Cycle Model by Nachmias & Felbinger, proposed in 1982, is the first framework I analysed. The reason is that most later frameworks have been extensions or adaptations of this framework in one way or another.
Then, its modified version of the big data-revised policy cycle by Johann, Peter and Ralph, proposed in 2016, has been analysed. While the framework is one of the first to advocate using big data in policy formulation and analysis, it also had a limited approach regarding the role of political willingness in decision-making. The justification for proposing this theoretical framework was more interesting than the actual framework, which I explained later in the paper. Lastly, the Multiple Stream Framework (MSF) proposed by John Kingdon in 2011 has been analysed as it has a political stream component. The Punctuated Equilibrium Theory (PET) and Advocacy Coalition Framework did not answer the questions we intend to invoke in our research, because of which I have limited my analysis to MSF only.
On the one hand, the Advocacy Coalition Framework (ACF) deals with factors that lead to policy change, how it affects government agencies and their operations, how various actors learn from each other, and scientific and technical education during policy making. On the other hand, PET does not negate the policy cycle framework. It focuses more on stakeholder engagement and interaction during the process of agenda setting and the more significant impact of the feedback mechanism to explain the cause of policy stability and sudden change.
Thus, the central research questions in the research article can be summarised as follows:
Do the existing frameworks sufficiently explain policymaking, especially in the context of policymaking in large democracies like India?
What role does political willingness play in policymaking under such circumstances?
What could be the proposed layout to fill the gaps of non-linearity caused by the role of political willingness in policymaking?
This research uses the case study of India's Data Protection Regulation formulation process, analysing it and considering some prominent policy-making frameworks. This paper explains to policy science students that things get complicated when stakes are high in policymaking and how conventional academic frameworks fall flat. In the process, the paper attempts to propose a layout out of a framework which is lucid yet applicable in such circumstances.
2 Historical background
While the study of political science has a long history, Harold Lasswell’s call for a distinctive study of policy science in his work ‘The Policy Orientation’ played a crucial role in bringing awareness about the discipline.
Laswell, in his book, states, “We can think of the policy sciences as the discipline concerned with explaining the policymaking and policy execution process and with location data and proving interpretations relevant to the policy problems of a given period” , pp.15). Policy science, according to Laswell, encompasses three crucial aspects. The first is the study of policy processes through policy analysis. The second aspect involves the outcomes of policy studies, known as policy formulation. The third and final significant aspect of policy science involves the findings from disciplines that contribute most to the intelligence needs of the time. Laswell emphasized that the term “policy” refers to the most critical life choices and is not limited to government policy but extends to a broad range of social policies related to businesses and individuals .
We see a paradigm shift in Laswell’s works from political to policy science. Mainly during and after the Second World War, his works deal with problems of society and how various forms of science can contribute to resolving them. It was majorly due to his understanding of the development of academia and its practical use during wars. His writings explain how he mapped the evolution in various forms of academic domains and their use during the world war, how it helped the USA fight these wars and their aftereffects.
For him, the people of the whole world constituted a global community. At the end of the Second World War, there was a need for expanding social science academia to solve the fundamental problems of our civilizations, which are already disclosed through the application of scientific methods to understand various societies and cultures. For him, it was the genuine aspiration of democracy with a value-oriented approach, which he expected the future policy science experts to curate while expanding this academic domain. By value-oriented approach, Laswell referred to the need for solutions which were good for the larger community in any democratic society .
3 Literature review
Before we delve deeper into various theoretical public policy frameworks, it is essential to understand their more extensive classification and categorisations. The approach to academic classifications of public policies by Lowi, Dror and Dye is worth mentioning here. Lowi explained Public Policy into three main typologies: distributive, redistributive, and regulatory policies. According to him, Distributive Policies deal exclusively with the distribution of new resources, Regulatory Policies deal with policies regulating individual or societal behaviour, and Redistributive Policy deals with modifying the existing distribution of resources in any manner or form. His typology dealt more with the implementation process rather than focussing on the area and nature of intervention . Y Dror and other academicians agreed that policy science constitutes an interdisciplinary approach cutting across disciplines such as sociology, political science, public administration, and management sciences .
Later, Dye differentiated policies into various model-based theories, explained through various aspects of social life like an institution, pressure groups, etc. He differentiated public policy theory into Institutional Model, Rational Model, Incremental Model, Group Model, Elite Model and Process Model . The institutional model focused on policies as an institutional output resulting from the interaction between executives, legislature, and judiciary. The rational model was developed primarily for economic analysis and is based on optimal solutions from all available alternatives. The incremental model focused upon bounded rationality, i.e., along with rational choice, the knowledge and capability of decision-makers are considered. The group model focussed upon the role of interest groups, which acts as pressure group compelling policymakers to respond to their demands. Lastly, the elite model focuses on the needs of governing elites while it is assumed that the electorate is generally poorly informed about public policies, giving a free hand to the elites to shape public policies with hardly any form of citizen participation.
As depicted above in Fig. 1, the policy cycle theory mostly approached policymaking in multiple stages, cyclically occurring one after another. Though the theory has its utilities, scholars have advised against seeing them in isolation. The public policy analysis and formulation study through a cyclic model using all the stages of the Stage Heuristic Model (Laswell stage theory) is the post-positivism culmination of a single theoretical framework conceptualisation to understand policy formulation and analysis. It comprises Problem Identification/Agenda Setting, Policy Formulation, Policy Adoption, Policy Implementation, and Policy Evaluation stages. The boundaries of policy science cut across disciplines such as sociology, psychology, political science, public administration, etc. At various times, the analysis remains a bureaucratic exercise. At the same time, formulation and implementation depend on political will.
Various researchers have pointed out that public policy experts have remained distant from power centres where policy decisions are made in their quest to understand policy formulation and evaluation . The same is true for theories which ignore the political willingness and various allied factors that play a crucial role in any public policy. According to researchers, this model, in its current form, should not be used as a universal and formal description of policy analysis. On the other hand, various researchers have proposed the policy cycle as a generic disruption of the public policy process only [17, 21]. Research has found that policymakers have a prevailing sense that public policy formulation theories like the policy cycle should not be interpreted as a general formula that neatly and reliably explains the policy process .
Due to these reasons, there has been a need to revisit Harold Laswell and other policy scientist's ideas and approaches towards the post-positivism model of public policy framework, as it ignores the complex value-driven nature of the policy process as well as the fundamental roles of political power in determining the direction of policy analysis as well as policy formulation.
With the rising populist tendencies in global governance, a detailed understanding while mapping policy formulation and analysis is needed. A robust theoretical framework should be able to explain various facets of policymaking not only under ideal circumstances but under rising populist tendencies, too.
Noted political scientist John P Mccormick, through his book Machiavellian Democracy, explained and theorised for the first time how a populist democracy operates and how policy formulation takes place in populist democracies. He also proposes the need for democratic reforms in policymaking through a group of common citizenry without much interference from elites . Scholars like Radin and Howard tried to map the policy process in existing democracies. It became known as policymaking in the post-machiavellian world [9, 22].
In his book Beyond Machiavelli, Radin mentions Kautilya's counsel to Chandragupta, the Maurya Dynasty's founder. The advice recommends seeking input from three or four ministers for informed decision-making. Relying on a single minister might not yield clear solutions for complex matters, and involving more than four ministers could be burdensome and jeopardize confidentiality .
Cosmo Howard, in his interpretation of Radin’s notion of post-Machiavellian policy analysis, has concluded that though the model can be of use, it shall not be interpreted as a rigorous and formalistic guide to actual policy process as it ignores the contexts like political vested interest and agendas, bureaucratic vested interest and agendas, competition among various government departments, policy experts, civil society and various other stakeholders . In modern days, policymaking never occurs in greenfield sites, as there is always a preexisting policy that must be modified or overturned, leading to friction between existing and proposed policies.
However, academicians accept that the government's prime interest is announcing and implementing new policies, for which they earn kudos. With time, the political interest shifts as the policy progresses. Just like the role of political willingness is less discussed in policy formulation and analysis-related theories. Similarly, parts of the judiciary and bureaucracy are hardly well-demarcated .
Most of the policy frameworks, including the policy cycle and the revised policy cycle, which we will be discussing, include the policy discussion stage before the policy formulation stage as two separate stages. The perception or rough idea of any policy based on need is always there with what Howard termed Maxwell’s elite. More than not, policy formulation and discussion go hand in hand and should ideally not be seen as separate stages. In reality, just like Kautiliya's suggestion to Chandragupta, the initial policy draft is prepared by an assigned group of experts in committees appointed based on the feedback of very few experts, and once the rough draft is ready, it is made public for more extensive consultation . Policy discussions are based on these policy drafts. Post discussion/consultation, the inputs are included or ignored based on various factors.
As depicted in Fig. 2 below, the recently proposed big data-revised policy cycle by Johann, Peter and Ralph tried to revisit the post-positivism policy cycle model with how big data, or the large set of data collected across various user-based platforms analysed for better insights, can be incorporated into policy formulation and analysis exercises . This theoretical framework is an extension of the existing cyclic framework. According to the author, the addition is the real-time data analysis at every policy formulation stage.
The major challenge and drawback of using big data at all stages is that most data sources do not fully represent the population, especially in countries like India, where digital divide and exclusion are still significant challenges across demography, gender, age group and cultures. Further, the authors have not clearly explained how the framework will cater to the challenges of data privacy and security breaches in utilizing the users' real-time data at every stage.
As far as the use of real-time data is concerned, while social media-based content and other forms of real-time data might help identify the nature of the problem in the problem identification and agenda setting, there remains the need for detailed inputs from masses and experts in the designated committees in the policy formulation stage.
Crucially, the authors illustrate their theoretical framework with case studies from countries like China and Saudi Arabia. In these examples, big data was harnessed for mass surveillance and quelling civil rights movements, often disregarding user privacy concerns. These instances highlight the critical issue of user data privacy, undermining the authors' aim to establish a robust foundation for applying big data in policy formulation and implementation. The authors tend to place excessive emphasis on utilising big data, although it should be viewed as just one component in the initial stages of the process [10, 14].
Most existing theoretical frameworks often overlook the influence of political regimes on public policy formulation, with few exceptions like John Kingdon's Multiple Stream Framework, although it has its limitations . Kingdon's model introduces the political stream as one of the three streams—alongside problem and policy streams—where events align to create windows of opportunity for policy formulation and agenda setting. Within this context, the political stream primarily involves elected or appointed officials who engage in power dynamics shaped by three key factors: national sentiment, the role of interest groups, and government involvement.
It's important to acknowledge that adopting the Multiple Stream Framework (MSF) has inherent limitations and assumptions. The proposed theory, in contrast, is a modified version of the garbage can model of organizational choice, typically employed in corporate decision-making within management studies and focuses upon intital stages of policy cycle framework, thus is an extension to the existing framework. It has also found application in explaining policymaking in various government departments, notably in the United States and the United Kingdom, by different scholars .
One notable issue with the MSF is its reliance on windows of opportunity for policy formulation and the assumption that a policy alternative is available for all policy issues within the policy community. This framework often fails to capture the intricate interactions among various stakeholders within the governance machinery instrumental in shaping political will for policy formulation. It also overlooks the discourse between agenda-setting and the final decision-making stage in the policy process.
The multiple stream framework fails to map the underlying interaction between various stakeholders in governance machinery that creates a political willingness towards policy formulation. Moreover, in a democratic ecosystem, policy formulation does not occur linearly. It comprises layers of actions and reactions often not documented politically and sociologically in mainstream public policy discourse.
4 The need to revisit policy theories
Our literature review shows that theoretical frameworks in discussion, the Policy Cycle Model by Nachmias & Felbinger, the Big Data-Revised Policy Cycle by Johann, Peter, and Ralph, and the Multiple Stream Framework (MSF) by John Kingdon, encounter limitations when attempting to elucidate the non-linear policymaking process in vast democracies like India, where various stakeholders and political factors play a significant role.
It's essential to acknowledge that these theoretical frameworks are not redundant but have limitations, particularly when addressing the issue of undue delays in policymaking caused by non-linearity introduced by the role of political willingness. Furthermore, it's worth noting that most of these new or old frameworks share a common foundation in the Stage Heuristic Model proposed by Harold Laswell. This foundation includes the Policy Cycle Model, Big Data Revised Policy Cycle Model, MSF, PET, or ACF. Each time a new theoretical framework is introduced, it offers a unique perspective or solution to specific problems that the existing framework may not adequately address.
In our literature review, we explored the Policy Cycle Model by Nachmias & Felbinger, developed in 1982, which presents a cyclical process involving stages such as Problem Identification/Agenda Setting, Policy Formulation, Policy Adoption, Policy Implementation, and Policy Evaluation. However, this model oversimplifies policymaking's intricate and non-linear nature in complex democracies like India. It doesn't sufficiently account for the role of political will and power dynamics, which are crucial in policy formulation. Scholars have emphasized that this model cannot serve as a universal and comprehensive framework for policy analysis.
The Big Data-Revised Policy Cycle by Johann, Peter, and Ralph, introduced in 2016, seeks to incorporate real-time data analysis at every policy formulation stage, specifically emphasising utilizing big data. Nonetheless, this framework faces a significant challenge: most data sources do not adequately represent the entire population, especially in countries like India, where issues related to digital exclusion and the digital divide persist. Furthermore, it fails to address data privacy and security concerns adequately. Additionally, it places excessive emphasis on big data, overlooking the importance of expert input and the influence of political will in the policy formulation process.
Lastly, the Multiple Stream Framework (MSF) by John Kingdon, introduced in 2011, introduced the concept of the political stream as one of the three streams, alongside the problem and policy streams, in policy formulation. It considers factors such as national sentiment, the role of interest groups, and government involvement. Nevertheless, MSF relies on windows of opportunity for policy formulation and assumes that a policy alternative is readily available for all policy issues, which may not hold in complex democracies like India. Additionally, it falls short of capturing the intricate interactions among various stakeholders that shape political will in the policy formulation process.
In extensive democracies like India, policymaking is influenced by many complex, non-linear factors, including political dynamics, public sentiment, and intricate stakeholder interactions. The existing theoretical frameworks, while valuable in addressing specific aspects of policymaking, do not sufficiently account for this complexity. Thus, there is a pressing need for further refinement to understand better and navigate these nuances and complexities, providing a more comprehensive understanding of the policymaking process in diverse and dynamic political landscapes.
Thus, there exists a need for what Thomas Kuhn would call a paradigm shift to revisit the existing, theoretical aspect of policymaking to resolve the various aspects discussed .
5 Case study of the data protection regulation formulation in India
To understand the issues more clearly from the lens of discussed framework, we have analysed the case study of a policy formulation process from India. The policy being much debated Data Protection Regulation for India. The motivation behind the case study of the personal data protection regulation formulation in India lies in the fact that it was duly covered in the press lately, thus providing enough resources to map the process.
India's discourse on data protection regulation has been ongoing, but the country is still formulating a comprehensive data protection regime. The judgment in 2017, K.S. Puttaswamy vs the Union of India, where privacy, for the first time, was recognized as a Fundamental Right in India, was the central turning point in the privacy discourse in India.
Subsequently, in August 2017, the Ministry of Electronics and Information Technology (MeitY) formed a committee of experts led by Justice BN Srikrishna to deliberate and propose a suitable data protection framework for India.
Since then, multiple policy drafts have been brought forth before and after extensive consultations with various stakeholders. This complex process has involved navigating roles, encountering friction among different stakeholders, and highlighting the crucial role of political willingness in shaping data protection policy in India. Despite the continuous efforts and interventions since 2018, India still awaits a data protection law. The formulation of such a policy goes beyond the conventional policy explanation due to its complexity and the numerous challenges involved.
In July 2018, the Justice BN Srikrishna committee submitted its report, which laid the foundation for subsequent data protection bills. One of the key outcomes was the proposal for the Draft Personal Data Protection Bill, 2018. MeitY sought stakeholders' feedback on the Draft Protection Bill, allowing inputs from civil society, individuals and subject experts .
After several consultations and revisions, a revised version of the bill, i.e., the Personal Data Protection Bill 2019, was presented in the Lok Sabha in December 2019 . This version incorporated feedback from various stakeholders and addressed concerns raised during the review of the 2018 version. Due to its complexity, the bill was referred to a Joint Parliamentary Committee (JPC) in December 2019 for detailed examination.
The JPC was responsible for conducting comprehensive bill reviews and holding numerous meetings, discussions, and consultations. Experts, industry representatives, civil society organizations, and the general public were all invited to provide insights and perspectives on data protection, privacy, and data processing practices in India.
After 2 years of intense examination, the JPC submitted a review report on December 16, 2021. This report contained 93 recommendations on existing provisions and proposed 81 amendments to the bill. Additionally, another panel led by a Union Minister recommended 97 corrections and improvements to the draft bill .
However, due to severe criticism and concerns from experts and privacy activists, the Central government withdrew the Personal Data Protection Bill 2019. The Minister for Electronics and Information Technology moved the motion to withdraw the bill in Lok Sabha. The government intended to introduce a new global standard law with a comprehensive legal framework to consider proposed amendments and address contemporary and future challenges.
Following the withdrawal, the government released the Digital Personal Data Protection Bill, 2022, which consisted of 30 clauses. Floated in November earlier that year, this new bill was expected to be tabled in Parliament's Monsoon Session starting on July 20. The Union Cabinet approved the draft Bill . The bill was not placed in Lok Sabha for approval; a relatively new draft of Digital Personal Data Protection Bill 2023 was introduced in the Parliament and passed by both the houses of the Indian parliament, i.e. Lok Sabha and Rajya Sabha.
Figure 3 depicted above explaines the overall non-linearity introduced in the data protection policymaking process as discussed in the section.
6 The proposed policy framework
As discussed earlier, many of the new and established frameworks draw from a common foundation, namely, the Stage Heuristic Model by Harold Laswell. We find the Policy Cycle Model, the Big Data Revised Policy Cycle Model, MSF, PET, and ACF. When a novel theoretical framework emerges, it brings a fresh perspective or solutions to address specific issues that the existing framework might not adequately cover and ours is no exception.
Even in the case of our proposed theoretical framework, most of its components bear a resemblance to the Stage Heuristic Model elements which have been used in the Policy Cycle and Big Data Revised Policy Cycle Models. However, it's important to note that proposed framework goes beyond the surface similarities to delve into the intricacies within each stage, particularly emphasising the pivotal role of political willingness at the centre of policymaking explaining the non-linearity and delay in policy making as we saw in Fig. 3 earlier. This clarification should dispel any confusion regarding the fact that our proposed framework is entirely self-constructed, without drawing from existing scientific literature or insights.
If formulation was done through the conventional or revised policy cycle theoretical framework, we should have seen coherence and linearity in various stages of policy formulation, which was missing. Further, political willingness causing non-linearity in the process could not be mapped.
Based on our findings from the case analysis, the role and placement of political willingness within the policymaking theorisation becomes crucial. Indeed, as depicted in Fig. 4 below, the proposed framework is an adaptation that follows the basic structure of the conventional policy cycle theory. The nobility in the framework comes from introducing political willingness at the centre of the policy-making process and its ability to end the loop and restart the process afresh. It is vital because, as discussed, political willingness significantly influences the implementation or non-implementation of public policies, particularly regarding agenda setting, which can vary depending on the nature of the problem, whether it is perceived, related to political ambitions, or influenced by lobbying.
6.1 Problem identification
The problem identification stage is also known as agenda setting in various previous public policy formulation and analysis frameworks. It is the first stage of the proposed policy framework. There are mainly two types of policy problems that the policymakers have to map: existing and perceived. The existing problem refers to issues or challenges recognized and acknowledged as genuine and significant by policymakers, experts, and the large masses. More than often, in an era of digital technology and platforms, the perception can be picked from people's responses over social media platforms.
The second type of policy problem can be classified as the perceived policy problem. It includes issues that may not necessarily be objectively identified as policy constraints but are perceived as such by specific stakeholders or interest groups. It could also be a forecast issue as per the extrapolation from existing data by the experts. Lastly and most importantly, these could be political agendas or political and corporate lobbying outcomes.
Perceived problems can be categorised into forecasted issues, political agendas and lobbying outcomes. The forecasted issue refers to anticipated challenges and policy roadblocks. Based on extrapolating existing data and trends, experts use these predictions to proactively address emerging concerns and make informed decisions for effective long-term governance. The political agenda here refers to policy issues that may or may not exist but are part of political leaders or their parties' objectives and priorities. The ruling government's political ideology, party manifesto, or electoral promises often drive these agendas. Implementing such policies leads to the political parties or government in power gaining more political support and mass support and helps them consolidate their political power. Lastly, lobbying efforts deal with issues that gain attention and priority due to the influence of lobbying by a specific interest group, industry players, corporate entities or advocacy organizations. It is no hidden secret that lobbying is the groups actively lobbying with policymakers to influence policymaking occasionally to take care of their corporate interest.
6.2 Policy formulation
The policy formulation stage is the second stage in the proposed framework. Formulation of expert committees for in-depth consultation and deliberation with stakeholders is an inherent part of this stage. We see the creation of various committees entrusted with duties to create the initial policy draft, which the concerned ministry or department then sends through their website for public consultation. Usually, the concerned departments and committees conduct in-person consultations to understand the matter better. The rigour of the policy formulation depends upon the allocation of resources and the degree of consultation with stakeholders. It depends upon the permutation and combination of relationships between various stakeholders, including political elites.
6.3 Policy adoption
The policy adoption stage involves finalising the policy draft. Both consultations are held during the policy formulation stage. Usually, the updated draft is approved to be tabled in front of the department meniscus ministry or is sent to the parliament for further cabinet approval so that it can be tabled in front of members of parliament for deliberations and approval. Sometimes, it is sent to the concerned standing committee, or in cases where no concerned parliamentary committee deals with the policy, a joint parliamentary committee is constituted for further deliberation and scrutinization. After that, again, the updated bill is tabled in the parliament for debate and approval of the lower house, followed by the upper house's approval. Ultimately, the bill becomes law and provisioning is done for its implementation, which we will discuss in the next stage.
6.4 Policy implementation
The policy implementation stage provides means for implementation and mass awareness. The provisioning of means includes creating vicious infrastructure and organisational setup needed to implement the policy and its regular evaluation and updating. Competition and friction among various stakeholders involved in the process often become the reason for poor implementation, leading to the failure of policies. Apart from political elites and lobbyists, stakeholders like the legislature, judiciary and executive members must always be mapped.
6.5 Policy evaluation
The policy evaluation stage is one of the crucial aspects of any policy implementation. First, impact evaluation comes into the picture to understand how well the policy has been implemented. Impact evaluation enhances policymaking and performance by providing independent, evidence-based assessments that promote transparency, accountability, and an opportunity for further policy improvement. It is always done closely with all the stakeholders and requires healthy people’s participation. The policy evaluation outcomes are used for better insights to improve policy deliverables and resource utilization.
6.6 Political willingness
In our proposed theoretical framework, political willingness takes centre stage across the public policy formulation cycle. It brings with it the political as well as sociological discourse on the type of agendas, roles of the various stakeholders involved in the process and their struggle for prominence in the policy formulation process, political agendas of the political parties as well as the corporate interests behind policies all of which when combined leading to a final policy for implementation.
In his book ‘Society Must Be Defended’, Michel Foucault wrote beautifully about the importance of discourse and the role of politics in governance and social dominance. For him, while politics is the continuation of war by other means, discourse is a weapon of power and an instrument of confrontation with realities .
The discourse on political willingness in policy formulation considering the roles of various governing elites involved in the process, their competition and friction cannot be ignored. Another issue is generalising policy science academia into managerial exercise packed under ‘public policy’ academia. When Laswell suggested that policy science experts should consider the practitioner's understanding of the process, he must have no idea that 1 day, there will be hardly any space left for policy science experts in the public policy academia . More than not, the governing elites responsible for policy implementation are usually too reluctant to analyse the problem through its political and sociological merits. They prefer the easy way out by reducing the whole domain into a governance valuation exercise. It keeps the political elites happy, as rightly pointed out by Laswell. They, too, are fighting for their prominence. Those responsible for implementing various policies are not necessarily subject experts at the administrative level.
Moreover, they prefer subject matter experts who are less critical of the policy proposal, leaving little room for improving the policy through critical analysis. It is like a never-ending loop of personal aspirations at various levels. More than not, academia and governance fail to differentiate between the limits of public administration and policy science capabilities. While the earlier is meant to implement policies and govern these policies in ways to make them more efficient, it is the later academic discipline that deals with analysis and formulations.
Regarding the significant findings of our research in light of our research questions, firstly, the study highlighted that the prominent existing theoretical frameworks, such as the policy cycle, the big data-revised policy cycle and the multiple stream framework, have been valuable tools for understanding policymaking. However, in the context of complex policies in large democracies like India, these frameworks fail to explain comprehensively. They do not adequately address the complexities introduced by factors like political willingness, diverse stakeholder interests, and the influence of political agendas.
Secondly, through the literature review and case study analysis, the research showcased how political willingness plays a central and dynamic role in policy-making, introducing non-linearity in the overall process. The study highlights how political willingness influences the agenda-setting, policy formulation, and implementation stages.
Lastly, to address the gaps and non-linearity introduced by political willingness, this research proposes a framework that places political willingness at the core of the policy formulation process. This proposed framework acknowledges the multifaceted nature of political willingness, encompassing political agendas, lobbying efforts, and the influence of various stakeholders. Doing so provides a more holistic and nuanced perspective on policymaking. This model highlights the need for ongoing evaluation and adaptation as political willingness and societal dynamics evolve.
This research is an attempt to invoke debate on the viability of other theoretical frameworks in light of political willingness and the inherent non-linearity it brings in the process, often leading to delay in the policy outcome or ending up in the final policy outcome which is in complete contrast with what the situation demands.
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Manazir, S.H. Reimagining public policy formulation and analysis: a comprehensive theoretical framework for public policy. Discov glob soc 1, 16 (2023). https://doi.org/10.1007/s44282-023-00018-4