The Global State of Democracy Indices: Technical Procedures Guide

This Guide outlines the process of constructing the Global State of Democracy (GSoD) Indices, which inform the annual Global State of Democracy Reports. It is written for readers who want a better understanding of the Indices, as well as researchers who may wish to use and build on the data contained within them. This Guide builds upon the GSoD Indices Methodology. For a detailed outline of the conceptual and measurement framework see The Global State of Democracy Indices Methodology: Conceptualization and Measurement Framework, Version 9 (2025) (Skaaning and Hudson 2025).

For each component of the construction of the GSoD Indices, the Guide provides information about the procedures followed, outlined in a step-by-step description to allow the interested reader to understand the research process. The eight steps can be summarized as follows:

1. Identifying the data sources (see Chapter 1).
2. Preparing the data sources for merger (see Chapter 2).
3. Preparing the indicators for aggregation (see Chapter 3).
4. Assessing the unidimensionality of the Indices (see Chapter 4).
5. Aggregating the indicators into Indices (see Chapter 5).
6. Scaling the Indices (see Chapter 6).
7. Computing the confidence intervals (see Chapter 7).
8. Conducting validity checks (see Chapter 8).

After a brief description of the theoretical structure that guided this project, the Guide presents the data sources, the coverage of the data set, the indicators used to construct the main attributes and subattributes of democracy, the procedures used to compute these attributes and the structure of the complete data set.

Background

The main objective of the GSoD Indices is to identify systematic data that captures trends at the global, regional and national levels for different aspects of International IDEA’s comprehensive understanding of democracy. The Indices turn a broad range of empirical indicators from various data sets into measures of different aspects (attributes, and subattributes; see Figure I.1) of democracy that can be used to evaluate the state of democracy at the global, regional and national levels. The Indices can also help stakeholders, such as policymakers, researchers and civil society actors, in their analysis of trends related to different aspects of democracy and to identify priority policy areas. The Global State of Democracy 2023: The New Checks and Balances (International IDEA 2023) is an example of how the GSoD Indices can be used to track trends in democratic development. Version 9 of the GSoD Indices (2025) covers 174 independent countries in the period 1975–2024. The Indices are composite measures based on 154 indicators from different kinds of extant data sources. These indicators are assigned to the different subattributes in the conceptual framework and combined into the GSoD Indices using either item response theory (IRT) modelling, Bayesian factor analysis (BFA) or mathematical operations such as multiplication and averaging. A key feature of the methodology is that it generates uncertainty estimates for most of the Indices, which allows users of the data set to assess whether the differences in scores over time and across countries are significant.

The GSoD Indices were produced by a team of International IDEA staff and two external experts. The initial design of the Indices was supervised by an Expert Advisory Board consisting of five leading experts in the field of democracy measurement. To ensure consistency, transparency and high levels of measurement validity and reliability, careful justification and documentation of the conceptual distinctions and methodological choices made, and the use of state-of-the-art procedures were emphasized at all stages of the construction of the Indices.

Conceptual structure

The GSoD Indices build on an elaborate conceptual framework that is rooted in International IDEA’s State of Democracy (SoD) Assessment Framework (Beetham et al. 2008). The SoD Assessment Framework is defined by two principles (popular control and political equality), seven mediating values (participation, authorization, representation, accountability, transparency, responsiveness and solidarity), and four pillars (citizens, law and rights; representative and accountable government; civil society and popular participation; and democracy beyond the state). For more details, see the SoD Assessment Framework (Beetham et al. 2008) and the GSoD Indices Methodology (Skaaning and Hudson 2025). The democratic principles underpinning the SoD framework—popular control and political equality—are also at the core of the GSoD Indices. In order to construct the GSoD Indices, however, the SoD conceptual framework was modified to transform it into a systematic, cross-national and cross-temporal quantitative measurement tool (Skaaning and Hudson 2025).

The GSoD Indices aim to capture the extent to which (a) there is effective popular control over public decision makers, or vertical accountability; (b) citizens have politically relevant freedoms and power resources; (c) executive power is checked effectively by other powers, or horizontal accountability; (d) public authorities are impartial and predictable in implementing the law; and (e) people have and make use of various opportunities for political participation at different levels (Skaaning and Hudson 2025).

The GSoD data set therefore contains separate, fine-grained Indices related to four attributes of democracy (see Beetham 1999: 154–57; Beetham et al. 2008: 27–28):

1. Representation (free and equal access to political power).
2. Rights (individual liberties and resources).
3. Rule of Law (predictable and equal enforcement of the law, and limitation of government power).
4. Participation (instruments for and realization of political involvement).

In addition, the GSoD conceptual framework contains, at lower levels of abstraction, 17 subattributes and an additional 5 subcomponents of Civil Liberties and 3 subcomponents of Political Equality. Figure I.1 presents a schematic overview of the GSoD conceptual framework. Separate GSoD Indices are constructed for each attribute and subattribute (see Table I.1).

International IDEA’s GSoD Indices are composite measures based on 154 indicators collected from 22 different data sets. No original data collection was carried out in connection with the initial construction of the GSoD Indices. However, since 2018, International IDEA has coded new years for several of the data sets that have not been updated by their original authors.

A number of criteria guided the selection of the data sets to be used for collecting the indicators. First, only those data sets that provided transparent and credible information on data-generating processes were selected. Second, data sets were needed with extensive coverage both spatially (at least 130 countries) and temporally (at least 10 years in the period 1975–2024). Third, data sets were also needed that have been and will continue to be updated on a regular basis. Fourth, priority was given to open-access data sets.

Four different types of sources were used to collect data for these data sets:

1. Expert surveys (ES), for which country experts generate data based on their assessment of the situation regarding particular issues in a given country.
2. Standards-based in-house coding (IC), which is used by researchers to generate data based on an evaluative assessment of country-specific information collected through desk research from various reports, academic publications, reference works or news sources.
3. Observational data (OD) on features that are directly observable and do not need the interpretation of experts or researchers, such as infant mortality rates or the representation of women in parliament.
4. Composite measures (CM), which generate data based on a number of variables from different existing data sets rather than on original data collection.

For a more detailed discussion of the advantages and disadvantages of the various types of data used see Skaaning and Hudson (2025). Table A.1 (see Annex A) lists the 22 data sets used, the type of data they offer and the number of indicators collected from each of them.

The unit of observation for International IDEA’s Global State of Democracy Indices is the country–year. The 2025 release of the data set contains data for 174 countries for up to 50 years, giving a total of 8,176 country–years. Details of the spatial and temporal coverage of the data set are presented in the following sections.

Spatial coverage

International IDEA’s GSoD Indices provide data for almost all the independent countries in the world with a population in 2024 of at least 250,000 people. Countries are included in the data set from 1975 or, if the country was not independent in 1975, the year it gained independence (see Step 2). The data set does not include semi-sovereign units such as Somaliland. Countries that have been dissolved remain in the data set until they cease to exist. Using these inclusion rules, the data set is composed of 174 countries. The only country that has exited the data set is the German Democratic Republic, which was dissolved in 1990 after unification with the Federal Republic of Germany.

A complete list of the 174 countries included in the GSoD Indices data set is provided in Table B.1 (see Annex B).

Temporal coverage

The ninth edition of International IDEA’s GSoD Indices covers the period 1975–2024. The data set covers the period since the International Covenant on Civil and Political Rights and the International Covenant on Economic, Social and Cultural Rights took effect in 1976, using 1975 as a reference point. Moreover, the data set starts with a period that is often referred to as ‘the third wave of democratization’ (Huntington 1991) in the democratization literature.

The number of countries covered each year by the GSoD Indices is shown in Figure 2.1, while Table B.1 (see Annex B) indicates the temporal coverage for each of the 174 countries included in the data set.

Geographical regions

International IDEA’s GSoD Indices also include aggregated values at the regional and subregional levels. The regional definitions follow closely those developed by International IDEA for the Global State of Democracy (see International IDEA 2017), which creates regions based on a geographical criterion while also taking account of the cultural and historical links among countries that belong to the same subregion or region. In total, aggregated values are presented for a total of 5 regions and 18 subregions:

1. Africa (East Africa, Central Africa, North Africa, Southern Africa, and West Africa).
2. Americas (Caribbean, Central America, Northern America, and South America).
3. Asia and the Pacific (Central Asia, East Asia, South Asia, South-East Asia, and Oceania).
4. Western Asia.
5. Europe (Central Europe, Eastern Europe, Southern Europe, and North/Western Europe).

It should be noted that the values in the data set are aggregated without using population weights. Table B.2 (see Annex B) contains a complete list of the regions and subregions, as well as their membership.

Regional organizations

International IDEA’s GSoD Indices also contain aggregated values for five regional and international organizations: the African Union (AU), the Association of Southeast Asian Nations (ASEAN), the European Union (EU), the Organisation for Economic Co-operation and Development (OECD) and the Organization of American States (OAS).

As in the case of the geographical regions, the values in the data set are aggregated without using population weights. A complete list of the regional organizations and their membership is given in Table C.1 (see Annex C).

The GSoD Indices conceptual framework guided the selection of the indicators to be included in the data set. The indicators rely on various types of sources and are collected from extant data sets compiled by different organizations and researchers. The main priority in the selection of indicators was a high level of concept–measure consistency, or the extent to which the indicators really capture the core meaning of the particular concepts in question. In addition, the aim wherever possible was to select multiple indicators for each subattribute—especially where an adequate observable indicator was not available.

Assuming the selection of high-quality indicators, a cumulative approach to measurement generally improves confidence in the scores since it utilizes the combined efforts of various data providers in order to make the resulting measures more nuanced and reliable. The use of different indicators enables the capture of related, but nonetheless distinct, aspects of the features to be measured. It also tends to reduce the influence of idiosyncratic measurement errors associated with individual indicators. Finally, drawing on several indicators makes it possible to assess the level of agreement between indicators and use this information to calculate uncertainty estimates for the Indices (see Pemstein, Meserve and Melton 2010; Fariss 2014).

Each of the 154 indicators selected from the 22 data sources corresponds to one of the subattributes or attributes of democracy developed for International IDEA’s GSoD Indices. Tables in Annex D detail the indicators used in each of the aggregated indices, the sources of these indicators, and the coverage and extent of missingness for each indicator (see Annex D). There is an extensive discussion on the selection of indicators in Skaaning and Hudson (2025), while detailed information on each of the indicators is available in Tufis and Hudson (2025).

Both this step and the following step translate the theoretical links between the attributes and subattributes, and between the subattributes and indicators, into corresponding aggregation formulas. The GSoD framework is based on the assumption that the more the principles are met, the more democratic a political system is. Thus, the achievement of these principles is not seen as an either/or matter, but as a matter of degree. The choice made of the measurement process—to construct Indices with relative, fine-grained scales and uncertainty estimates but without substantive thresholds—also aligns better with this gradualist perspective than crisp distinctions.

Reflective aggregation models and formative aggregation models were used to combine the various indicators into composite Indices. Where indicators of the theoretical constructs reflected a common underlying variable and/or generally showed very high levels of covariation, aggregation based on item response theory (IRT) models or Bayesian factor analysis (BFA) was used. For a detailed discussion of the distinction between formative and reflective indicators, and for detailed descriptions of the aggregation methods used, see Skaaning and Hudson (2025).

It should be noted that, regardless of the aggregation method used, the goal in all cases was the same: to use the information contained in multiple indicators that are measuring different facets of the same phenomenon to construct an index that measures better than its composing indicators the phenomenon that is to be measured. Both IRT and BFA are data reduction procedures that combine the various interrelated indicators into a single measure.

Many of the selected indicators were expected to cluster in meaningful ways and to tap into a limited number of overarching concepts. These expectations were based on theoretical grounds and because previous dimensionality analyses of these (and related) indicators have shown that many of them are highly correlated and reflect common latent dimensions (see e.g. Skaaning 2009; Møller and Skaaning 2014a, 2014b; Teorell et al. 2016).

Item response theory

IRT modelling was used at the lowest level of aggregation (subattribute or subcomponent level) if there was a significant amount of missing data (more than 5 per cent) in any of the indicators used to reflect the concept in question. This allowed use of multiple indicators of the same latent concept ‘to identify and correct for measurement error, and to quantify confidence in the reliability of our estimates’ (Pemstein et al. 2015: 30). A lack of overlap in the coverage of indicators does not result in missing values in estimates for the affected country–years, as would be the case if using factor analysis. Using full information maximum likelihood IRT models means that all the relevant information from the indicators can be used. The missing data in some indicators are then reflected in the uncertainty estimates, which also reflect the level of agreement between indicator scores, or the extent to which they are correlated. If none of the indicators provides data for a given country–year, no estimate is calculated for this country–year. The mirt package developed by Philip Chalmers (2020) was used to conduct the modelling.

Bayesian factor analysis

If there is virtually perfect overlap in the measures, then BFA becomes a more viable option. Like IRT models, BFA provides point estimates for the latent dimension and confidence intervals, but it does so only for country–years with uniform indicator coverage. BFA was therefore used to combine indicators only if all of them had a low level of missing data. Moreover, when applicable, BFA was used to combine subcomponent scores into subattribute scores, and thereafter subattribute scores into attribute scores (i.e. if the measures were expected to reflect the same latent concept in the framework, and when the indicators/Indices to be aggregated showed very strong correlations). For BFA the MCMCpack package in R was used (Martin, Quinn and Park 2020).

Formative approach

When indicators are understood as constitutive components of the concept of interest, this means that the indicators are not necessarily highly correlated. Here, the use of a formative approach is more plausible. Formative models were used in the GSoD framework when a particular version of the procedure was judged to be more appropriate than purely reflective procedures. Hence, a formative model was used to combine the contestation index with an inclusiveness indicator to create the Representation index, and a formative model was also used to aggregate indicators related to Local Democracy (see Step 5: Aggregating the indicators into Indices).

As indicated in Chapter 4, two different methods were used to aggregate indicators into Indices—IRT and BFA. The choice between the two methods was determined solely by the proportion of missing values in the indicators included in each index. In the case of the two subattributes that are themselves aggregated from subcomponents (Civil Liberties, and Political Equality) that include indicators, the indicators were first aggregated into the subcomponents and the subcomponents then into subattributes.

When using IRT as an aggregating procedure, the scores were estimated using the mirt package in R. Box 5.1 is an example of the syntax used to estimate the scores. The mirt package gives two estimates for each index—the estimate and its standard error for each country–year that has at least one valid value on the indicators that compose the index. The estimates and their standard errors are saved in an output file and then imported into the data set where they can be used for additional computations if needed.

When using BFA as an aggregating procedure, the scores were estimated using the MCMCpack package in R, more specifically the MCMCfactanal command. Box 5.2 provides an example of the syntax used to estimate the scores. For each index, the MCMCpack package gives two estimates, the estimate and its standard error, for each country–year that has valid values on all the indicators that compose the index. The estimates and their standard errors are saved in an output file and then imported into the data set, where they can be used for additional computations if needed.

The aggregation rules used for each subcomponent, subattribute and attribute computed are shown in Table 5.1.

All the Indices in the data set offer nuanced scores in the form of interval scale measurement. The Indices have been normalized (Step 6) to range from 0 (lowest achievement among all the country–years) to 1 (highest achievement among all the country–years). A score of 0 generally refers to the worst performance in the whole sample of country–years covered by a particular index, while 1 refers to the best country–year performance in the sample. For a number of Indices, however, 0 also has an absolute meaning as the lowest score that is theoretically possible. The subattribute Indices capturing Credible Elections, Elected Government, Direct Democracy and Local Democracy all have substantively meaningful minimum values that refer to the total absence of the features in question.

For most indices, the yearly scores for each country are accompanied by uncertainty estimates, which can be used to assess whether differences between countries and within countries over time are significant. These uncertainty estimates, in the form of confidence intervals or margins of error, reflect the statistically likely range for the country–year index scores based on the indicators used.

The GSoD Indices confidence levels refer to one standard deviation below and above the estimated score. This means that about 68 per cent of the ‘true’ values would be found within these intervals. Confidence intervals are only available for those Indices that are constructed from multiple indicators. The more the underlying indicators are in agreement regarding the scoring (high–low) of a particular aspect of democracy, the narrower the confidence intervals. The more the underlying indicators are in disagreement, the wider the confidence intervals.

If the confidence levels overlap when comparing the scores for two or more countries on the same GSoD (attribute) index, the difference between the scores is less than two standard deviations and is therefore not statistically significant at the p=0.05 level. Similarly, overlapping confidence intervals for different years when comparing the scores of one country for a particular GSoD index also indicate that the difference is statistically insignificant. More generally, short-term fluctuations are hard to capture and should be interpreted with caution, while it is usually possible to be certain about longer-term trends.

A series of validity checks was run throughout the process of constructing the GSoD Indices to verify that they truly measure what they are thought to be measuring. Comparisons with extant measures should indicate a high degree of correspondence between the GSoD measures and existing measures of the same phenomena.

This Guide presents the technical procedures used to construct International IDEA’s Global State of Democracy Indices to provide a guide for those who want a better understanding of how the Indices were constructed and, eventually, for those who want to use, modify or build on the Indices. It provides a detailed step-by-step description of the procedures used, allowing the interested reader to follow each step.

It should be noted that this technical methodology only provides information about the method used to construct the Indices. The information presented in this document builds on and is complemented by an elaborate discussion of the conceptual background to the Indices in the GSoD Indices Methodology (Skaaning and Hudson 2025). If the reader is interested in a particular indicator used, more information is available in the GSoD Indices Codebook (Tufis and Hudson 2025). These three documents taken together provide complete information on the GSoD Indices.

The Annexes present additional information that some readers or users of the GSoD Indices might find useful, including a complete list of the data sets used as data sources (Annex A); a list of the countries, regions and subregions in the GSoD data set (Annex B); a list of the regional organizations included in the GSoD data set (Annex C); the attributes, subattributes and indicators included in the GSoD data set (Annex D); and the results of the unidimensionality and validity tests performed during the creation of the GSoD data set (Annexes E and F).