Policy Capacity and Local Disaster Management Effectiveness: Assessing Institutional Performance in Rokan Hulu Regency, Indonesia

Disaster risk governance is one of the most challenging areas of public administration, requiring cross-sector coordination, rapid decision-making under conditions of uncertainty, and the continuous integration of disaster risk reduction (DRR) into development planning at various levels of government [1-4]. Despite numerous Disaster Management Plans (DMPs) and decentralization reforms, policy effectiveness remains uneven: local institutions may comply with formal requirements yet achieve only limited reductions in vulnerability and risk exposure [5-7].

These gaps indicate institutional, not merely technical, issues. Drawing on the concept of policy capacity, this article analyzes how analytical, operational, and political capacities interact to shape disaster management effectiveness at the local community level, and why some policy frameworks do not translate into consistent, measurable outcomes [8-10].

Rokan Hulu Regency is a substantial case for scrutinizing the relationship between capacity and effectiveness. Located in northern Riau Province, it covers an area of 8,230 km² and has a population of 573,520 persons. The region's biogeophysical conditions, characterized primarily by watershed and peatland ecosystems, rendered it highly vulnerable to hydrometeorological hazards, including floods, landslides, and forest or land fires.

To reduce these risks, the local government established its Disaster Management Plan for Rokan Hulu 2025–2029, consistent with the National Disaster Management Master Plan (NDMMP) 2020–2044. The Disaster Management Plan has three main objectives:

1.    Strengthening institutional coordination and governance capacity,

2.    Enhancing community-based disaster preparedness, and

3.    Developing resilient infrastructure and ecosystems.

These goals are understandable and appropriate, but remain constrained by institutional and budgetary realities. The Disaster Management Plan of Rokan Hulu even recognizes its chronically under-skilled technical workforce, dispersed data, and reliance on centrally provided transfers as impeding the delivery of long-term resilience.

These restrictions reveal a more profound problem of an imbalance in policy capacity: operational capacity (for implementing emergency response) is often strong, whereas analytical and political capacities are weak [11-13]. This asymmetry leads to short-term reactivity and long-term ineffectiveness—a pattern characteristic of disaster governance systems that are primarily prompted by emergencies rather than preventive planning [14-16].

Empirical Issues for Local Disaster Response

The Disaster Management Plan of Rokan Hulu 2025–2029 and the Disaster Risk Assessment (DRA) of Rokan Hulu 2023—2027 highlight several recurring governance and capacity issues. Policy capacity can be further described along three dimensions: analytical, operational, and political [9, 10, 17, 18]. The explanation can be seen in the following Table 1.

Table 1. Empirical challenges in local disaster management in Rokan Hulu Regency

Note: Regional Disaster Management Agency (RDMA); Disaster risk reduction (DRR).

Source: Author’s synthesis based on Disaster Management Plan of Rokan Hulu 2025–2029, Disaster Risk Assessment (DRA) of Rokan Hulu 2023–2027, and stakeholder interviews (2024).

Analytical Perspective: Policy Capacity and Effectiveness

The above empirical facts show that disaster institutional performance cannot be judged solely by the formal structures, mechanisms, and compliance with laws. It needs to be analyzed in terms of capacity and effectiveness: that is, how effectively public actors can mobilize analytic knowledge, operational capabilities, and political legitimacy in support of policy objectives [19-21].

The policy capacity framework is advantageous in this regard, as it acknowledges that capacity is multifaceted within complex governance systems.

1. Analytical capacity is comprised of technical expertise as well as cognitive ability to define problems, analyze data, and design policies.
2. Operational capacity represents the management and administrative resources needed to translate, monitor, and revise programmes.
3. Political capacity revolves around maintaining support from interested parties, forming alliances, and obtaining legitimacy for decisions [17].

In a decentralized setting such as Indonesia, these capacities are interlinked. Local governments could, for example, have high operational capacity (i.e., they perform well during crises) but low political and analytical capacity (e.g., weak data use, unstable commitment). These asymmetries help explain why so much regional policy appears administratively performant but strategically ineffective.

Public policy effectiveness is defined as the extent to which a policy achieves its anticipated outcomes or costs [22-24]. In disaster response, this includes both outcome-level measures (e.g., exposure to hazard mitigated; early warning systems versus other previous practices) and process-level measures (e.g., coordination of all stakeholders; learning loops) [25-28]. The relationship between capacity and effectiveness can be illustrated conceptually as:

1. Strong analytical capability → better evidence-based decision making and prioritization.
2. High operating schedules → optimize program delivery and logistics coordination.
3. Political capacity → legitimacy, resource, and sustainability.

If these three elements function well, policy effectiveness will increase through increased consistency, learning, and adaptation. Conversely, if one dimension is weak (in part due to strong operations but weak analytics), it may produce short-term outcomes through initiatives, but may not yield sustainable impact. A similar pattern can be observed in disaster management performance in Rokan Hulu, where a sustained reduction in disaster vulnerability did not correspond to a strong emergency response.

Emerging Theoretical Problem

The above findings highlight an analytical gap in how local governments understand and implement disaster policies. The Disaster Management Plan of Rokan Hulu 2025–2029 is predominantly a tool for measuring success through administrative achievements such as completed plans, numerous training activities, and disbursed funds rather than through assessments of institutional learning and resilience outcomes. This “input–output” bias reflects weak analytical and political capacities, including a lack of reflexivity, which are essential for adaptive governance [29-31].

For policy studies, Rokan Hulu offers an informative case of what Wu et al. [32] refer to as a capacity–effectiveness mismatch, that is, when there is a high degree of procedural compliance alongside low levels of performance. The problem is not the absence of a plan, but the insufficient capacity to translate policy into ongoing collective action [32-35].

Therefore, the analytical purpose of this study is 'to assess how differences in analytical, operational, and political capacities determine local disaster policy effectiveness in Rokan Hulu Regency'. It also aims to make a theoretical contribution to the broader discussion of policy capacity as a determinant of successful policies in decentralized systems.

Analytical Gaps in Design and Implementation of Policy Measures

The Disaster Management Plan of Rokan Hulu 2025-2039 is the result of a well-defined monument for disaster management planning, but its application was found lacking in analysis and institutionalization. These discrepancies reveal a mismatch between the rational policy capacity prescribed in planning and the actual policy capacity that unfolds during implementation [36-38].

At the analytical level, the absence of an integrated risk information system impedes evidence-based policy [39, 40]. The Disaster Management Plan for Rokan Hulu is dominated by descriptive hazard mapping, without a spatial–economic assessment of risk. As a result, mitigation priorities are based primarily on administrative convenience rather than vulnerability. Population exposure, infrastructure at risk, and socioeconomic sensitivity data are not consistently combined, preventing the government from conducting cost–benefit analyses for prevention investments [41-43].

At the tactical level, the Regional Disaster Management Agency (RDMA) of Rokan Hulu is skilled at immediate logistics and mass action but disorganized in monitoring rehabilitation. There is more of a ‘project logic’ than a ‘policy cycle logic’ in implementation [32], such that, once projects end, lessons are rarely documented and used iteratively to learn.

Politically, local-level commitment to DRR fluctuates with shorter-term changes in leadership. This makes it difficult to sustain discussions on policy issues emphasized by local parliamentary policymakers, such as infrastructure and the economy, and even more so for DRR programs, which are considered secondary expenditure commitments. The Disaster Management Plan action plans are seldom considered in the Regional Medium-Term Development Plan or in annual budgeting, indicating limited political capacity and low institutionalization of DRR within policy processes.

Overall, these two gaps suggest the coexistence of an enduring capacity–effectiveness paradox: Rokan Hulu’s organizations possess the capacity to produce technically sound plans but struggle to realize their policy outputs. This dilemma is not specific to Rokan Hulu but is symptomatic of a widespread challenge surrounding decentralized policy structures that are fragmented along analytical, operational, and political axes.

Research Problem and Objectives

Given these shortfalls in analytical and institutional capacity, the central problem this study addresses is: How does policy capacity—analytical, operational, and political—vary, and what are the consequences of such variation for local disaster management policy effectiveness in Rokan Hulu?

Three related research questions are derived from this line of inquiry:

1. To evaluate the existing state of analytical, operational, and political capability for disaster management institutions in Rokan Hulu.
2. To assess the implications of these capabilities for realising DRR outcomes with the Disaster Management Plan of Rokan Hulu 2025–2029.
3. To find out strategic priorities for enhancing institutional performance and policy learning towards more effective and sustainable local disaster governance.
4. These goals are designed to push past just descriptive evaluations of disaster programs, and toward a more conceptual understanding of why some policies work—or not—given a particular institutional environment.

Policy Capacity–Effectiveness Problem Matrix

To structure the empirical diagnosis, the study uses a policy capacity–effectiveness problem matrix (Table 2) to map observed capacity gaps onto effectiveness constraints. The detailed operationalization of indicators and the evidence base used to populate the matrix are explained in the Methods and reported in the Results section. The explanation can be seen in Table 2.

The problem matrix indicates that effectiveness constraints are unevenly distributed across capacity dimensions: operational routines may function adequately, whereas analytical learning and political support remain fragile. This implies that improving local disaster governance requires capacity integration, not isolated technical fixes.

1. From compliance to performance: Success is often calibrated in terms of adherence to procedures (number and quality of reports produced, number of activities completed, or spending targets achieved) by local government. However, policy performance requires outcome-based evaluation, which is linked to risk reduction and increased resilience.
2. From individual to systemic capacity: Training programs have not strengthened institutional learning capacity. Systemic barriers still exist (e.g., fixed budget lines, siloed coordination). Real capacity building is about more than individual skills; it also requires attention to institutional and political structures that support the adaptive management of ecosystems.
3. From short-term task performance to long-term learning: The main aim of most disaster programs supported in Rokan Hulu is rapid response. But effectiveness also depends on the extent to which institutions learn from each incident and translate those lessons into policy improvements. This is consistent with the view that policy learning is a bedrock of effectiveness.
4. From centralized to distributed authority: There is a formal decentralization of DRR, but fiscal and political control over DRR policy stays concentrated. The building of local political capacity — whether through the establishment of cross-sectoral forums for inclusive governance, broad access by communities to the political process and decisions through support for participatory budgeting, or public accountability — has the potential to enhance both legitimation and responsiveness.

Table 2. Policy capacity–effectiveness problem matrix in Rokan Hulu Regency

Note: Disaster Management Plan; Regional Disaster Management Agency (RDMA); Disaster risk reduction (DRR).

Source: Author’s synthesis from the Disaster Management Plan of Rokan Hulu 2025–2029, the Disaster Risk Assessment (DRA) of Rokan Hulu 2023–2027, and interviews with local institutions (2024).

This research begins with a central question: why is disaster management in a decentralized context often ineffective despite the existence of formal structures and legal mandates at the local level? The study argues that the key problem lies in an imbalance in policy capacity: relatively strong instrumental/operational capacity but weak analytical and political capacity, creating a bottleneck to policy performance. Therefore, improvements cannot be achieved through technical training or organizational restructuring; instead, strengthening analytical reflexivity and political coherence within the local governance system is necessary.

The significance of this research is twofold. Empirically, this study maps policy capacity in Rokan Hulu, identifying the dimensions that most constrain effectiveness and the relationships among them. Theoretically, this research strengthens the link between policy capacity and policy effectiveness in decentralized disaster governance, with lessons relevant for other subnational governments in Indonesia and the ASEAN region. By integrating an evaluative framework, policy capacity is positioned not merely as a managerial issue but as a key factor in policy performance and resilience.

Research Design and Approach

This study employs a mixed‑method case study design grounded in the policy capacity framework. Data were drawn from (1) documentary sources, (2) a structured institutional self‑assessment survey administered to key officers involved in DRR planning and implementation, and (3) semi‑structured interviews and focus group discussions with representatives of the agencies listed in Table 6. The design supports sequential triangulation by using survey profiles to guide qualitative probing and by validating perception‑based findings against documentary and stakeholder evidence.

1. Documentation review that includes reviewing the Local Disaster Management Plan of Rokan Hulu 2025–2029 and its complement, a document known as the Disaster Risk Study of Rokan Hulu 2023–2027, the Regional Development Plans 2021–2026, and the RDMA annual reporting.
2. A structured capacity questionnaire developed from the policy capacity diagnostic framework to conduct institutional self-assessment was implemented among local agencies working in disaster management.
3. Semi‑structured interviews and focus group discussions were conducted with purposively selected informants representing the RDMA, Regional Development Planning Agency, relevant sectoral agencies (e.g., public works and social affairs), as well as Non-Governmental Organization (NGO) and university stakeholders. Selection prioritized officials with direct responsibility for DRR planning, budgeting, inter‑agency coordination, or program delivery. Data collection followed a common topic guide aligned to the three capacity dimensions; sessions were documented (field notes and transcripts) and analyzed through thematic coding.

This design enables triangulation among formal policy instruments, self-assessed institutional capacities, and practitioner perceptions, a method conducive to policy research.

Table 6. The agencies responsible for policy design and implementation

The research focuses on Rokan Hulu Regency, a medium-sized local government in Riau Province, Indonesia, that has been repeatedly affected by floods, landslides, and peatland fires. The Disaster Management Plan of Rokan Hulu 2025–2029 identifies 16 subdistricts as being at medium-to-high disaster risk. Institutionally, the study examines five core agencies responsible for policy design and implementation. More detailed information can be seen in Table 6.

This institutional scope reflects a cross-sectoral configuration, consistent with disaster management’s whole-of-government nature.

Policy Analysis

The first step involved systematic document analysis of key planning instruments. Following the policy analysis approach proposed by Dunn [22], the study extracted data on policy objectives, implementation mechanisms, and evaluation indicators. Each document was reviewed using a coding framework with three dimensions of capacity:

1. Analytical indicators (e.g., data use, evidence base, analytical staff, policy coherence).
2. Operational indicators (e.g., Standard Operating Procedures, coordination routines, timeliness, monitoring mechanisms).
3. Political indicators (e.g., budget allocation, stakeholder participation, political commitment).

The coding was conducted using NVivo 14 to organize and categorize textual evidence from more than 1,000 pages of planning and evaluation documents.

Institutional Self-Assessment Survey

A structured institutional self‑assessment questionnaire was distributed to 30 respondents using a purposive (near‑census) approach targeting key officers across the agencies in Table 6 who are directly involved in DRR planning and implementation. Because the population of such officers is limited at the regency level, n = 30 represents the accessible core of decision‑makers rather than a statistical sample intended for population inference. Items used a 1–5 Likert scale across 12 indicators (four per dimension). Throughout the manuscript, the term institutional self‑assessment survey is used consistently to refer to this perception‑based instrument (Table 7).

Table 7. Indicators of policy capacity used in the survey

Responses were aggregated to produce average scores for each institution and dimension. The data were analyzed descriptively (means and variances) and compared with secondary evidence from policy documents to assess the coherence between perception and practice.

Data Analysis Techniques

The analytical process followed a sequential triangulation logic. First, the institutional self‑assessment survey was summarized descriptively (mean, dispersion) to map capacity profiles across agencies. Second, documentary evidence and interview/focus group discussion materials were coded thematically to explain, contextualize, and challenge the quantitative patterns. Third, integration was achieved through cross‑validation (joint interpretation), where convergent and divergent evidence was documented to strengthen the credibility of inferences about capacity–effectiveness linkages. Given the small-N, the quantitative strand is used for profiling rather than hypothesis testing.

1. Descriptive quantitative analysis to summarize institutional self-assessment scores.
2. Qualitative coding and thematic analysis of documentary and interview data.
3. Cross-validation between qualitative themes and quantitative patterns to ensure interpretive coherence.

More detailed information can be seen in Table 8.

Table 8. Analytical framework for data triangulation

This triangulation enhances reliability, as multiple sources and perspectives support each proposition. Policy effectiveness is defined in this study as the extent to which policy outputs and outcomes achieve intended objectives, relative to available resources and expectations [22, 32].

Reliability and Triangulation

Three approaches were used to ensure the reliability:

1. Inter-rater agreement – standardized questionnaires and coding manuals were used in all centres.
2. Cross-source validation-comparing documentary evidence to survey and interview information.
3. Inter-coder reliability-Translation as well as qualitative coding was double-coded by two separate investigators.

Face validity was strengthened by grounding the indicators in established policy capacity frameworks and widely used effectiveness criteria, and by aligning item wording with the documentary indicators extracted from the Disaster Management Plan and related planning instruments. Consistency in interpretation was supported through the coding manual, joint review of indicator definitions, and cross‑source triangulation between perception‑based responses and documentary/interview evidence.

Limitations of the Research Design

Although the multi-method design enhances the depth of analysis, several limitations should be noted:

1. Temporal constraint – The analysis is based on capacity and effectiveness for a single planning cycle (2025–29). A longitudinal effects study would provide more compelling evidence of learning.
2. Perceptual bias – Surveys could be biased to self-assess the performance of an institute in a better light. Triangulation reduces, but cannot eliminate, such a bias.
3. Data access – Some of the agencies were circumspect about financial details, which limited comprehensive budgetary analysis.
4. Generalization – The case study approach emphasizes depth to add an element of generalizability through replication rather than through statistical sampling.

However, these limitations do not affect the validity of the conclusions; instead, they highlight the contextual complexity of investigating policy capacity in decentralized governance systems. Contextual insight and analytical rigor are integrated into the methodological framework. It provides an organised method for studying the influence of various capacity dimensions (analytically, operationally, and politically) on the effectiveness of the local disaster management policy process.

More detailed information about the methodology used can be seen in Table 9.

Table 9. Summary of the methodological framework

Note: Disaster Management Plan; Regional Disaster Management Agency (RDMA).

This integrated approach provides both the breadth of measurement and the depth of explanation required to assess institutional performance in public policy contexts. In summary, the methodology of this study reflects a hybrid between policy analysis and institutional evaluation, consistent with the analytical traditions of contemporary public administration. By integrating multiple data sources and analytical perspectives, the study aims to provide a comprehensive understanding of how local governments—specifically the Rokan Hulu Regency—translate policy capacity into effective disaster management outcomes.

The following section, Results and Discussion, will apply this framework to analyze empirical findings, evaluate variations in capacity and effectiveness across institutions, and discuss their implications for strengthening local disaster governance.

The authors examine how differences in analytic, operational, and political policy capacity affect the local disaster-governance system in Rokan Hulu Regency, Indonesia, in managing disasters. Applying a mixed method – including document analysis, institutional survey, and stakeholder interview – the results suggest an asymmetrical capacity structure. Readiness is relatively high, as evidenced by efficient emergency coordination, logistics, and community response. Nevertheless, analytical and political capabilities are weak, particularly in data integration, evidence-based planning, and consistent budgetary or legislative support. The PEI substantiates this myopia: strong in the short term but weak in longer-term learning and institutionalization. This pattern reflects an ability–effectiveness paradox in which groups demonstrate competence in administrative outputs but have limited strategic impact. Our results confirm the theoretical proposition that high-performance policy effectiveness is not a function of any single capacity but instead of the dynamic interplay among the analytical, operational, and political capacities.

Theoretical and Practical Implications

In theory, this study advances the policy capacity–effectiveness nexus by showing that, in decentralized disaster governance, high operational capacity can coexist with only moderate policy effectiveness when analytical learning and political anchoring are weak and poorly integrated. Empirically, the Rokan Hulu case illustrates how fragmented data, limited evaluation routines, and discontinuities in budget and legislative support can prevent Disaster Management Plan ambitions from translating into sustained risk‑reduction outcomes. In practice, this implies that strengthening DRR requires institutional arrangements that link evidence, implementation, and political authorization through the planning–budget–monitoring cycle.

Policy Recommendations

1. Develop analytical capacity: (0–12 months) institutionalize an inter‑agency data and policy analysis function (RDMA–Development Planning Agency at Sub-National Level–Central Bureau of Statistics and sectoral agencies) with standard data‑sharing protocols; (12–24 months) build staff competencies for risk modelling and policy evaluation; and implement routine monitoring and annual analytical reviews that directly inform the next Disaster Management Plan update.
2. Strengthen operational integration: Consolidate coordination mechanisms into clear Standard Operating Procedures and performance routines across agencies; standardize reporting and after‑action reviews so that operational learning feeds the planning cycle; and use practical digital tools to track program implementation and inter‑agency responsibilities.
3. Political commitment and legitimacy: Secure DRR budget anchoring through multi‑year local regulations or budget tagging; formalize cross‑sector stakeholder engagement (including village and community representatives) to sustain legitimacy; and strengthen legislative oversight through a dedicated forum/committee that reviews DRR performance and financing.
4. National–local synergy: Align regency‑level capacity strengthening with provincial and national support by prioritizing technical assistance for analytical functions, incentives for integrated data systems, and guidance that links local planning indicators to national DRR priorities while preserving local discretion for context‑specific implementation.

Limitations and Future Research

The study relies on a small, purposively targeted institutional survey (n = 30) and cross‑sectional evidence; findings are therefore diagnostic rather than causal and are not intended for statistical generalization. Future research should apply longitudinal designs, larger comparative samples across regencies, and stronger inferential tests to examine how capacity reforms translate into sustained effectiveness over time.

In summary, improving the effectiveness of disaster policy in Rokan Hulu requires a capacity‑integration reform that integrates analytical evidence, operational coordination, and political legitimacy within a single planning–implementation–learning cycle. While the findings provide a grounded diagnosis of where integration breaks down in a decentralized setting, the evidence base remains institution‑specific and cross‑sectional; expanding comparative and longitudinal analyses is essential to confirm how reforms affect effectiveness over time.