Government at a Glance: Southeast Asia 2025

Annex A. Methodology for the Infrastructure Governance Indicators

The OECD Infrastructure Governance Indicators (IGIs) are intended to support and monitor the implementation of the OECD Recommendation on the Governance of Infrastructure (hereafter “the Recommendation”), adopted by the OECD Council on 17 July 2020 (OECD, 2020[34]). The Recommendation is based on 10 pillars that relate to how governments plan, prioritise, fund, budget, deliver, operate and monitor infrastructure assets. It presents a whole-of-government approach, covering the entire life cycle of infrastructure projects and placing special emphasis on regional, social, resilience, environmental perspectives and the gender perspective. The overarching nature of the Recommendation’s pillars allows for exhaustive analysis of the multiple governance dimensions that are at play in infrastructure planning, decision making and delivery. They therefore provide a robust conceptual framework for the development of the IGIs. The pillars represent both conceptual categories and functional areas of work. As such, the pillars are not standalone entities and interact with one another to support a comprehensive overview of infrastructure governance.

The IGIs serve as a diagnostic tool to help countries assess their current stage of development and identify the dimensions that may require more attention. In particular, the IGIs aim to achieve the following goals:

• map OECD countries’ state of play regarding infrastructure governance, identifying strengths and weaknesses

• provide tools for countries to self-assess their performance in each of the infrastructure governance pillars highlighted in the Recommendation

• provide a comprehensive view and deeper understanding of the different pillars that compose the infrastructure governance framework

• allow countries to identify changes in their performance on infrastructure governance through time

• draw attention to how much data are available and needed to measure infrastructure governance, as well as the benefits of building a comprehensive database in the field

• contribute to the discussion on the relationship between infrastructure governance and infrastructure outcomes.

In addition to a general assessment, the IGIs also serve to pinpoint specific areas within each pillar that may require further development from each country. Results at a more granular level (i.e. performance on the sub-components of each dimension) allow for a more in-depth assessment.

The methodology used for building the IGIs is based on the Handbook on Constructing Composite Indicators (OECD/European Union/EC-JRC, 2008[35]). It has also been shared and discussed with experts and public officials from the Network of Senior Infrastructure and PPP Officials (SIP) and the Working Party of the Leading Practitioners on Public Procurement (LPP).

Structure of the IGIs

The IGIs are measured and presented in composite indicators, one for each of the pillars arising from the Recommendation, plus the cross-cutting pillar on environmentally sustainable and climate-resilient infrastructure. Each pillar can be disaggregated into groups of variables, called sub-pillars. These sub-pillars reflect countries’ performance at a more granular level. The nested structure helps countries understand the driving forces behind each of the composite indicators. An overview of the sub-pillars under each of the four pillars implemented in Southeast Asia (SEA) is shown below in Figure A.1Figure A.1.

Figure A.1. Infrastructure Governance Indicators: Pillars implemented in Southeast Asia and their sub-pillars

Implementation of the IGIs

The IGIs were implemented in OECD countries using ten composite indicators covering each of nine pillars of the Recommendation and the cross-cutting pillar on environmentally sustainable and climate-resilient infrastructure. The development of a composite indicator to measure the remaining pillar on coordination across levels of government is currently being discussed with the OECD Regional Development Policy Committee (RDPC). The results for the full set of indicators will provide an overarching analysis of countries’ performance across all dimensions of the Recommendation and on the cross-cutting pillar on environmentally sustainable and climate-resilient infrastructure. Results of the IGIs for OECD countries are available in the OECD Infrastructure Toolkit (OECD, n.d.[36]).

The IGIs were implemented in four SEA: Indonesia, the Philippines, Singapore, and Thailand. Data was collected during 2023-2025, covering four pillars: long-term strategic vision, value for money, public procurement, and environmentally sustainable and climate-resilient infrastructure.

Data collection and validation

The IGIs were built using data collected via OECD survey instruments, namely the Survey on the Governance of Infrastructure and other relevant data collected from OECD policy communities. Data presented in this publication is drawn from the 2023-2025 OECD Survey on the Governance of Infrastructure in SEA. The survey was designed based on inputs from relevant divisions/directorates of the OECD and in consultation with the SIP and the LPP. Relevant institutions to coordinate the data collection exercise in each of the SEA countries were identified. Invitations to participate in the project were sent out to relevant institutions. Respondents were predominantly senior officials in the central/federal ministries of infrastructure, public works and finance, as well as in infrastructure agencies and other line ministries.

Various steps were undertaken to ensure the highest standards in data quality and accuracy. Workshops were organised in Indonesia and the Philippines to help respondents in answering the survey, introducing the IGIs, its methodology, and results from OECD countries, sharing experiences from OECD countries on the implementation, presenting the questionnaire used for data collection, the key terms used, and sharing caveats to keep in mind and tips on how to answer the survey. Additionally, the OECD Secretariat provided further clarification and instructions on how to answer the survey to respondents in the four SEA countries. A data validation process was used to check for internal and external consistency in the survey responses, comparing the answers to data from other sources and verifying that supporting evidence was systematically provided before validating the responses.

Selection of variables and re-coding

The sub-pillars were constructed from a set of variables that aim to measure the adoption and adequacy of governance practices in line with the Recommendation. The variables were selected in order to measure countries’ performance in infrastructure governance in terms of inputs and processes (e.g. policy tools, norms of interaction, decision-making methodologies and monitoring strategies). The proposed composite indicators did not include variables related to outputs or outcomes (e.g. levels of investment, quality of infrastructure services, or amounts of capital stock and achievement of policy objectives). It is important to note that the selection of variables and re-coding, and thus the structure of the composite indicators, could be subject to change in future editions of the IGIs to account for changes in institutional, political and economic settings across OECD countries.

The OECD Surveys on the Governance of Infrastructure were designed to collect qualitative data. Therefore, the responses to the survey questions were re-coded using numerical values between 0 and 1, where 1 is the maximum value and indicates complete alignment with the best practices highlighted in the Recommendation, and 0 is the minimum value indicating the absence of such practices in the country.

Missing data

Due to the cross-cutting nature of the concept of infrastructure governance, the OECD Surveys on the Governance of Infrastructure require respondents from different institutions to provide information on the infrastructure governance frameworks and practices in a country. The composite indicator for each pillar was not calculated for countries that reported not having the information to answer two or more survey questions for any one of its sub-pillars. Consequently, those countries were not included in the OECD average indicator value for that pillar. As the data used to build the composite indicators are qualitative, data imputation was not used to deal with missing data. However, it should be noted that where country responses were only based on practices applicable in a certain sector or sectors, these were retained and important caveats provided in relation to those.

Weighting and aggregation

To build the composite indicators, all the sub-pillars within each pillar were given equal weight. However, the variables within a sub-pillar were weighted differently depending on: 1) the number of variables that make up each sub-pillar, as the larger the number of variables within a sub-pillar the lower the weight each variable will have; and 2) the relevance of each variable, where greater weight was given to variables that are more relevant in measuring a specific sub-pillar. The weights assigned to the variables in each sub-pillar add up to 1. The weighted scores of all the variables are totalled to arrive at a sub-pillar score that ranges from 0 to 1. The linear aggregation method was used to first aggregate the variables into a sub-pillar (i.e. weighted arithmetic mean), and then the sub-pillars into a composite indicator (i.e. arithmetic mean). Experts and public officials from the SIP and the LPP were consulted over the assignment of weights and the aggregation type before the final set of weights was confirmed.

Multivariate analysis

Multivariate analysis was employed to study the overall structure of the data collected in OECD countries. The analysis was used to further help guide methodological choices with respect to variable grouping and aggregation. As such, the analysis was used in OECD countries where the methodology was first developed and not in SEA countries where the existing methodology was applied. The techniques used in the multivariate analysis are detailed below.

Factor analysis

Factor analysis was used to check the structure of the data along the variable dimension, to help identify groups of variables that are statistically similar and that could be regrouped under a sub-pillar where such grouping is conceptually relevant. The analysis was run separately for each pillar. Principal component factor analysis was used to extract the principal components and consider them as factors (groups of variables). The groups of variables offered by the factor analysis were interpreted together with the conceptual framework underpinning the composite indicators exercise.

The results were reviewed to look for any set of variables that measure the same underlying dimension and that could be regrouped to avoid double-counting. The results offered several cases where the factors matched well the conceptual groupings (sub-pillars). In the case of variables with high levels of covariance but belonging to different initial conceptual groupings, the results were discussed with experts to determine if the variables needed to be regrouped. Following this consultation with experts, the sub-pillars were either maintained or restructured to align with the conceptual framework.

Cronbach coefficient alpha

The Cronbach coefficient alpha (c-alpha) was used as a measure of internal consistency and scale reliability. The coefficient shows how related variables are as a group and to what extent they measure the same underlying concept. A c-alpha of 0.7 is usually recommended as an acceptable reliability threshold (Lafortune and Ubaldi, 2018[37]). The c-alpha test was used to measure internal consistency for each pillar.

Sensitivity analysis

To assess the robustness of the composite indicators, Monte Carlo simulations were used to study how uncertainty in the weighting schemes affects the composite indicator values. This technique uses 1 000 sets of randomly generated simulated weights to calculate possible composite indicator scores for each country under different weighting schemes.

Measuring balance in sub-pillar scores

Good infrastructure governance requires improvements across multiple dimensions. Ideally, countries should make progress in all sub-pillars, and low scores in some should not be compensated with high scores in others (i.e. sub-pillars for a country should not show a wide range of values). For each pillar, a rating scale based on the coefficient of variation was used to rate country profiles from balanced (low variability in country sub-pillar scores under a pillar) to unbalanced (high variability in country sub-pillar scores under a pillar). For each pillar, this analysis shows how balanced country profiles are with respect to sub-pillar scores and helps identify countries with relatively high indicator values but with great variability in sub-pillar scores. Analysis for each OECD country is in the OECD Infrastructure Toolkit. (OECD, n.d.[36])

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