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docna: 0: docna: Reviewing Assessment Tools for Measuring
            Country Statistical Capacity
docty: Policy Research Working Paper
theme: Data production, accessibility and use,Data Development and Capacity Building,Institutional strengthening and capacity building,Public Sector Management
abstracts: cdata!: Country statistical capacity is
            increasingly recognized as crucial for development, but no
            academic study exists that reviews the available assessment
            tools. This paper offers the first review study that fills
            this gap, paying particular attention to data and practical
            measurement challenges. It compares the World
            Bank&#x2019;s recently developed Statistical Performance
            Indicators and Index with other widely used indexes, such as
            the Open Data Inventory index, the Global Data Barometer
            index, and other regional and self-assessment tools. The
            findings show that each index brings advantages in data
            sources, number of indicators, measurement focus, coverage
            of countries and time periods, and correlation with common
            development indexes. The Open Data Inventory index covers
            the most countries, the Global Data Barometer index collects
            data through its surveys, and the Statistical Performance
            Indicators and Index offer a broader framework for assessing
            statistical systems. The paper offers further thoughts on
            the potential mechanisms through which these tools can bring
            positive impacts on economic activities and some political
            economy concerns, as well as future directions for development.
display_title: Reviewing Assessment Tools for
            Measuring Country Statistical Capacity
listing_relative_url: /research/2024/03/34276185/reviewing-assessment-tools-measuring-country-statistical-capacity
topicv3: Agriculture and Food Security m1326205 435,Education m1327048 470,Macroeconomic Modelling and Statistics
            m1504530 3108
publishtoextweb_dt: 2024-03-06T00:00:00Z
dois: 10.1596/1813-9450-10717
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
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model_extractions: Policy Research Working Paper 10717

# Reviewing Assessment Tools for Measuring Country Statistical Capacity

_Hai-Anh H. Dang John Pullinger Umar Serajuddin Brian Stacy_ Development Economics Development Data Group March 2024 A verified reproducibility package for this paper is available at http://reproducibility.worldbank.org, click **here** for direct access.
Policy Research Working Paper 10717

# **Abstract**

Country statistical capacity is increasingly recognized as crucial for development, but no academic study exists that reviews the available assessment tools. This paper offers the first review study that fills this gap, paying particular attention to data and practical measurement challenges. It compares the World Bank’s recently developed Statistical Performance Indicators and Index with other widely used indexes, such as the Open Data Inventory index, the Global Data Barometer index, and other regional and self-assessment tools. The findings show that each index brings advantages in data sources, number of indicators, measurement focus, coverage of countries and time periods, and correlation with common development indexes. The Open Data Inventory index covers the most countries, the Global Data Barometer index collects data through its surveys, and the Statistical Performance Indicators and Index offer a broader framework for assessing statistical systems. The paper offers further thoughts on the potential mechanisms through which these tools can bring positive impacts on economic activities and some political economy concerns, as well as future directions for development.

This paper is a product of the Development Data Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at hdang@worldbank.org. A verified reproducibility package for this paper is available at http://reproducibility. worldbank.org, click **here** for direct access.

<!-- Start of picture text --> TRANSPARENT<br>ANALYSIS<br>G<br>R E<br>N<br>S<br>I P<br>Y E<br>K A<br>C A<br>R P<br>LI R<br>O E<br>O C<br>W R<br>H<br>S<br>P<br><!-- End of picture text --> _The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **Reviewing Assessment Tools for Measuring Country Statistical Capacity**

Hai-Anh H. Dang, John Pullinger, Umar Serajuddin, and Brian Stacy<sup>*</sup>

# **Summary**

We review assessment tools for measuring country statistical capacity and offer further thoughts on their future development.

**Key words** : statistical capacity, statistical performance, statistical indicators, statistical capacity index, national statistical system

**JEL** : C8, H00, I00, O1

* Dang (hdang@worldbank.org; corresponding author) is senior economist with the Living Standards Measurement Study  Unit, Development Data Group, World Bank and is also affiliated with IZA, Indiana University, and London School of Economics and Political Science; Pullinger (johnpullingerltd@gmail.com) is former head of the UK National Statistical Service; Serajuddin (userajuddin@worldbank.org) is manager of the Indicators and Data Unit, Development Data Group, World Bank; Stacy (bstacy@worldbank.org) is economist with the Indicators and Data Unit, Development Data Group, World Bank. We would like to thank Lisa Bersales, Mustafa Dinc, James Foster, Haishan Fu, Craig Hammer, Johannes Hoogeveen, Dean Jolliffe, Christoph Lanker, Hiroko Maeda, Daniel Mahler, Jimmy McHugh, Yongyi Min, Johan Mistiaen, Yusuf Murangwa, Juan Oviedo, Viveka Palm, Carolina Sanchez Paramo, Francesca Perucci, Martin Rama, Walter Radermacher, Tara Vishwanath, and participants at the 63<sup>rd</sup> ISI World Statistics Congress, the 9<sup>th</sup> International Conference on Agricultural Statistics, and seminars at the World Bank for useful discussions. All remaining errors are ours alone and do not reflect in any way the viewpoints of colleagues who we discussed with. We would like to thank Juderica Dias and Dereje Ketema Wolde for helpful support with the data. We are grateful to the UK Foreign Commonwealth and Development Office (FCDO), formerly the Department of International Development, for funding assistance through various Knowledge for Change (KCP) grants.
# **Introduction**

A country’s statistical capacity is increasingly recognized as crucial for its development, for richer and poorer countries alike. Stronger statistical capacity results in better measurement of economic and social activities and outputs, which facilitates timely decisions by policy makers. Stronger statistical capacity can also contribute to better governance and accountability, where citizens are better informed about government activities and can be more engaged in the monitoring process. Indeed, the Sustainable Development Goals (SDGs) call for more capacitybuilding support to developing countries to help significantly increase the availability of highquality, timely, and reliable data (United Nations, 2023; SDG number 17.18). In a recent global flagship report, the World Bank highlights the strong, positive relationships between a country’s statistical capacity and the independence of its national statistical office (NSO) and its freedom of the press (World Bank, 2021).

Some practical examples can further illustrate the consequences of statistical capacity— stronger and weaker—in action. For the past decades, the U.S. Bureau of Labor Statistics has kept close track of the latest (un)employment trends in the economy and publicly releases these statistics every month. Data on employment trends keep all stakeholders well-informed, and typically feed into prompt actions by both the (interest-rate-setting) Federal Reserve Board and private investors in the stock market. Given the prominent role of the U.S. economy in the world, these actions typically reverberate overnight to other economies and stock markets around the world.

On the other hand, Roseth _et al._ (2019) observe that inaccuracies in the measurement of the municipal population resulted in approximately US$92 million in mis-targeted government budget transfer in El Salvador between 2000 and 2007. Weak statistical capacity not only causes inefficiencies but could lead to corruption and consequential damages to the economy. A notable case occurred when three state-backed Mozambican firms borrowed US$1.2 billion in government-guaranteed debt in 2013-2014—roughly 8 percent of the country’s GDP—without the parliament and the public’s knowledge. This lack of public debt transparency led to Mozambique facing severe restrictions in the international credit market when these loans were revealed in 2016 (Economist, 2019; IMF, 2019). More strikingly, a recent study estimates that, due to the incentive to overstate economic growth and a lack of checks and balances, autocratic regimes could overstate yearly GDP growth by 35 percent (Martinez, 2022).

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These examples allude to the fact that statistical capacity varies by country characteristics, particularly by country income levels. Specifically, using the World Bank’s poverty database, we plot the number of poverty estimates against a country’s income (consumption) level (as measured in household surveys) over the past 40 years in Figure 1. The fitted line for the regression of these data points on country income is positive and strongly statistically significant, suggesting that countries with higher incomes more frequently implement household surveys. Indeed, a 10 percent increase in a country’s average income is associated with almost two-thirds (i.e., 0.67) as many surveys. This gap of survey data is consistent with the observation that poorer countries, especially in Sub-Saharan Africa, tend to have weaker statistical capacity (Devarajan, 2013; Jerven, 2013; Sandefur and Glassman, 2015; <mark>Dargent</mark> _<mark>et al.</mark>_ <mark>, 2018)</mark> .

The international community has long placed much attention on assessing and improving country statistical capacity—particularly for (poorer) countries with weaker capacity—to better guide international support. The Statistical Capacity Index (SCI) was a tool developed by the World Bank in 2004 to assess global improvements in country statistical capacity (World Bank, 2020). Most recently, it was replaced by the Statistical Performance Indicators and Index (SPI), which offers a clearer conceptual framework and broader country coverage (Dang _et al._ , 2023). Other global assessment tools that have been employed for evaluating country statistical capacity include the Open Data Inventory index (ODIN) (Open Data Watch, 2022), the Global Data Barometer index (GDB) and its predecessor the Open Data Barometer index (ODB) (Global Data Barometer, 2022). Regional assessment tools (e.g., the Ibrahim Index of African Governance Statistical Capacity (IIAG)) and self-assessment tools were also employed by other international organizations for countries to self-report on their statistical capacity.

While these (assessment) tools offer policy makers different options to assess country statistical capacity, they typically follow different philosophical principles in generating their metrics as well as in collecting and processing data. It would be useful for various stakeholders— policy makers, national statistical offices, international development organizations, researchers, private investors, and others—to have a clear understanding of these differences, and to appreciate the relative strengths and weaknesses of the tools, for their most effective use. We thus offer the first critical assessment of these tools.

Using the SPI framework as the reference point, we review the tools’ guiding principles before discussing their coverage, groupings (dimensions), indicators, and data sources. To compare their 3
measurement power, we examine the relationship between these tools and representative development outcomes for each of the 17 SDGs, an overall SDG index, and several other common indexes. Finally, we offer some thoughts on other challenges, including potential mechanisms for their impacts on the economy, political economy factors, and future development of these tools. For illustration, we offer new analysis based on their latest data updates, including new data for the SPI in 2021 and 2022 that were not analyzed before.

# **Results**

We provide a brief overview of the various tools and highlight some key qualitative differences regarding their guiding principles, before offering a more detailed comparison over the dimensions, indicators, and countries covered by the tools. We subsequently examine the relationships of these tools with a number of key SDG outcomes and several other development indexes.

# **Overview of the tools and their guiding principles**

_Statistical Capacity Indicators and Index (SCI) and Statistical Performance Indicators and Index (SPI)_ Following the guiding principles that the source data should be publicly available and meet certain quality standards (e.g., as provided by the curators of the international databases), the SCI collected data from publicly available, international databases and NSO websites, with the major share coming from the former source. The SCI has been employed by different international and national agencies since its inception in 2004 to measure progress with development trends (United Nations, 2016), or areas of statistical improvement in member countries (OIC, 2012), or tracking the SDGs for child development (UNICEF, 2018). The SCI was widely employed in academic research covering different disciplines, ranging from economics, international development, political science to statistics. We offer in Appendix A, Table S1 a brief overview of some selected academic studies in the past decade that employ the SCI.

The SCI, however, has several key limitations, including an outdated framework, a focus on poorer countries only, and a lack of underlying conceptual and mathematical principles. The SPI recently replaces and offers several advantages over the SCI on both the conceptual and empirical 4
fronts (Dang _et al._ , 2023). In particular, the SPI explicitly offers standard _desiderata_ for a statistical index (i.e., simple, coherent, motivated, rigorous, implementable, replicable, incentive consistent). Conceptually, it consists of five pillars of data use, data services, data products, data sources, and data infrastructure, which can be further disaggregated into 22 dimensions. The SPI is also built on a clear mathematical foundation with a three-level nested weighting structure that offers desirable properties for an index such as symmetry, monotonicity, and subgroup decomposability (Cameron _et al.,_ 2021). Empirically, the SPI offers more than twice the number of indicators provided by the SCI. The SPI covers both low-income and high-income countries, while the SCI focuses on non-high-income countries alone. Finally, the SPI covers indicators related to the SDGs, while the SCI covers indicators related to the (older) MDGs.

Despite its infancy, to date the SPI has been adopted for measuring country statistical capacity in several high-profile policy reports such as _Sustainable Development Reports 2021, 2022, and 2023_ (Sachs _et al._ , 2021, 2022, and 2023) and the World Bank’s _World Development Report 2021_ (World Bank, 2021). In particular, the index was used to help highlight various gaps in countries’ data dissemination and openness, with higher statistical capacity levels being positively correlated with more NSO independence (World Bank, 2021). The index has inspired further research on assessing data openness and accessibility in MENA, a traditionally data-scarce region (EkhatorMobayode and Hoogeveen, 2022), or how best to construct measures for learning deficiency due to COVID-19-induced school closures (Azevedo, 2020), or to better understand how NSOs respond and adjust to the disruptions caused by the COVID-19 pandemic (Wollburg _et al._ , 2022). It also contributes to current thinking not only on improving the quality of NSSs, government use of data, and future official statistics (Radermacher, 2021; Asher _et al._ , 2022; Bersales, 2022), but also on other topics such as reducing GDP growth forecast errors (Gatti _et al._ , 2024), measuring public sector digital transformation (Dener _et al._ , 2021) and food and agricultural statistics (Bizier _et al._ , 2022), and selecting the appropriate context to measure student absenteeism and women’s empowerment (Yount _et al._ , 2022; Evans and Acosta, 2023).

We offer further discussion on the conceptual motivations and construction of the SPI in the Method section. We provide more details on the dimensions of the SPI, including ongoing data work, in Appendix A, Table S2 and a mapping of the SPI indicators to the SDG indicators in Table S3. We provide the latest update for the SPI country scores in 2022 in Appendix A, Table S4.

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Further comparison between the SPI and the SCI is provided in Dang _et al._ (2023) and in Dang _et al._ (forthcoming).

# _Open Data Inventory (ODIN) index_

The ODIN’s objective is “to provide an objective and reproducible measure of the public availability of national statistics, and their adherence to open data standards” (Open Data Watch, 2023). The ODIN currently provides annual data starting from 2015 but offers comparable data starting only from 2016. In contrast with the SPI’s data sources, the ODIN does not assess data published for countries on international organizations’ websites. Consequently, the ODIN focuses on data that are available from the official website of the NSO and any official government website that is accessible from the NSO site. It assesses the coverage and openness of statistics, where coverage refers to the availability of important statistical indicators classified into three main groups of social statistics, economic and financial statistics, and environmental statistics (which are further broken down into 22 topical sub-groups or data categories).

Each data category is assessed on five elements of coverage (i.e., availability of indicators and disaggregations, availability of data in the last five and 10 years, and availability of data at the first and the second administrative geographic levels), and five elements of openness (i.e., availability of data in machine-readable format and non-proprietary format, availability of reference metadata, availability of download options that make the data more accessible, and availability of an open data license or open data terms of use). Aggregate scores are computed across categories and elements, resulting in the overall ODIN score being an index of how complete and open an NSO’s data offerings are.

# _Open Data Barometer (ODB) and Global Data Barometer (GDB)_

The ODB provides “a global measure of how governments are publishing and using open data for accountability, innovation and social impact”. While first starting in 2013, the ODB has been further expanded to include other data aspects with its successor, the GDB, since 2017. Following a different approach from those of the SPI and the ODIN, the latest GDB collects primary data from an expert survey and supplements these primary data with other secondary data sources to generate its metrics (Global Data Barometer, 2022).

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The GDB offers an overall country score, a pillar score, and a module score. The country scores are built on four pillar scores for governance, capability, availability, and use and impact. The module scores are available for the following thematic topics: (private) company information, land, political integrity, public finance, public procurement, climate action, health, and COVID19. The GDB assigns different weights to each of its indicators, depending on whether it is a primary indicator (more weight) or secondary indicator (less weight) or which pillar it belongs to (i.e., governance, capability, and availability pillars have more weight).

# _Other Tools_

The Ibrahim Index of African Governance Statistical Capacity (IIAG) is a regional assessment tool aiming at “measuring African governance performance”, where governance is defined as “the provision of the political, social, economic and environmental goods and services that every citizen has the right to expect from their state, and that a state has the responsibility to deliver to its citizens” (Mo Ibrahim Foundation, 2023). The IIAG consists of four main pillars of governance: Security and Rule of Law, Participation, Rights and Inclusion, Foundations for Economic Opportunity, and Human Development. These categories are comprised of 16 sub-categories and make up the Overall Governance score.

Besides the global and regional tools, there are several detailed self-assessment tools.<sup>1</sup> These include the European Snapshot Tool, the UN NQAF (National Quality Assurance Framework) self-check tool, and the Paris21 NSDS (National Strategies for the Development of Statistics) selfassessment tool. Unlike the tools mentioned above, these self-assessment tools do not offer explicit performance scores.

In summary, our brief review above suggests several key qualitative differences across the different tools. As their names suggest, the SPI (and SCI) aims to measure country statistical capacity, the other indexes focus more on aspects of statistical capacity such as data availability and open data standards (the ODIN and the ODB and GDB) or governance performance (the

> 1 The World Bank has also produced self-assessment tools related to NSO efficiency and data openness, including the Improving the Productivity of National Offices for Statistics (IPNOS) toolkit discussed in Medina Giopp _et al._ (2020) and the Open Data Readiness Assessment (ODRA). The IPNOS toolkit provides a tool for evaluating the efficiency of national statistical offices along three pillars: budget and cost-efficiency of production; quality of process, products, and user’s satisfaction; and institutional and organizational aspects and has been applied in Costa Rica, El Salvador, and the Seychelles. ODRA is a readiness assessment designed to give national statistical systems a roadmap for creating or expanding an open data program.

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IIAG). The SPI, the ODIN, the GDB (and ODB), and the IIAG offer data points on country performance over time, while the self-assessment tools (including European Snapshot Tool, the UN NQAF self-check tool, and the Paris21 NSDS) are designed for self-assessment and do not offer such data.

The tools’ methods of data collection differ, with the SPI obtaining most of the data from curated international databases, the ODIN exclusively from NSO websites, the GDB mostly from expert surveys, the IIAG from a mix of different sources, and the three self-assessment tools from expert self-assessment tools. These different methods come with their own strengths and weaknesses. Data that are provided by the curators of the international databases potentially ensure data quality and comparability, especially in countries with weak statistical capacity where data are not well-maintained or where data might at times be subject to weaker quality control or even manipulation (as discussed earlier). But such data are dependent on the curators’ production cycles and hence at times can be a bit dated. On the other hand, collecting data directly from NSO websites or an expert survey could provide more in-depth analyses and uncover finer details, but it incurs high costs and could be far more time consuming. In addition, direct interviews of government officials might bias responses and complicate comparability across countries since government officials may have an incentive to overestimate their country’s statistical capacity where concerns exist regarding their ability to deliver, or they may underestimate their country’s statistical capacity where they are requesting additional international aid.

Notably, the SPI offers an explicit discussion of its conceptual and mathematical foundations in peer-reviewed academic publications (Cameron _et al._ , 2021; Lokshin, 2022; Dang _et al._ , 2023; Dang _et al._ , forthcoming). We employ the SPI’s five pillars as a reference point in the subsequent comparison of the different tools. We provide further discussion on the SPI framework in the Method section.

# **Further comparison of the tools**

# _Country coverage, dimension, indicators, and data sources_

We compare in Table 1 several other features of the tools (indexes), including their coverage of countries, dimensions, indicators and their data sources. For completeness, we show in this table both the current tools and their predecessors. In addition, we also broaden this comparison to include some self-assessment tools that have been employed by international organizations for 8
countries to self-report on their statistical capacity. These include the EU Snapshot Tool, the UN NQAF self-check tool, and the Paris21 NSDS self-assessment tool. But there is no publicly available information on how many years or how many countries for which these tools have been implemented. The only available information includes the (number of) indicators each tool employs, which we can compare against the SPI’s five pillars. Table S5 in Appendix A provides the links to all the tools discussed above (as well as briefly reviews some other tools on related development topics such as open government data, governance, poverty and human capital).

Several remarks are useful. First, except for the IIAG that focuses on Africa, all the remaining tools are global and use a scale of 0-100, with a larger number indicating better capacity. Regarding the number of countries (and years) coverage, the ODIN comes first (192 countries during 20162022), followed by the SPI (186 countries during 2016-2022), the SCI (145 countries during 20042020), the ODB (116 countries during 2013-2016, and 30 countries for 2017), the GDB (109 countries during 2021), and the IIAG (54 countries during 2010-2019). While the number of countries that an index covers can vary widely over the years, for comparison purposes, we show the number of countries covered by an index for its latest year available. All the indexes are generated annually, except for the GDB that is conducted biennially.

Second, except for the SCI and the IIAG that use simple weighting, all the indexes employ some form of nested weighting method to construct the overall score. The details of the nested structure differ significantly across indexes. The ODIN has a two-level nested structure with 65 indicators being categorized (under 22 smaller sub-groups) in three groups, and the ODIN overall score is an equal weighted average of these three group scores. The GDB also has a nested structure of pillars, indicators, sub-sections, and sub-questions, where the score at each level is a weighted average of its sub-components, which results in an overall weighted score. The SPI has a threelevel nested weight structure, and its overall score is based on a mathematical framework, where desirable properties of the index are explicitly discussed such as symmetry, monotonicity, and subgroup decomposability (Cameron _et al._ , 2021).

Finally, regarding the number of indicators, the three self-assessment tools as a group have more indicators than the other indexes, with the Paris21 NSDS self-assessment tool leading this group (149), to be followed by the EU Snapshot Tool (131), and the UN NQAF self-check tool (87) (the numbers in parentheses are the number of indicators). For the other tools, the GDB ranks first (55), to be followed by the SPI (51), the ODB (46), the ODIN (44), the SCI (21), and the IIAG 9
(3). Using the SPI’s five pillars as a reference point, the other indexes seem to cover either too much or too little for certain pillars. For example, the ODIN splits its indicators over two pillars, Data services and Data products, and has no indicators for the remaining three pillars. Only the GDB and the three self-assessment tools cover all five pillars, but with varying degrees for each pillar.

Yet, while the number of indicators provides a useful, quantifiable metric of statistical capacity for comparison, ultimately what this metric aims to capture is the different, quality aspects (dimensions) of statistical capacity. For this purpose, Table 1 also shows for each index the distribution of its indicators categorized by the five SPI pillars. Table 1 shows that all three selfassessment tools focus much more on the data infrastructure pillar (with at least 60% of their indicators) than the other pillars.

Figure 2 offers an alternative visual presentation to Table 1, where we plot the percentage of coverage for each SPI pillar for all the tools. Since a balanced distribution would require a coverage rate of 20% for each of the five pillars, comparing each index’s coverage against the light blue spider web in the background helps illustrate how much imbalance each index has. Again, this figure helps make clearer the patterns shown in Table 1. Compared to the SPI framework, the ODIN and the ODB focus more on data services and products, the IIAG and the SCI focus more on data products and infrastructure, and the GDB and all three self-assessment tools focus more on data infrastructure.

# _Measuring country progress over time_

The next question naturally arises: how do the tools keep track of country progress over time? We examine this question for the SPI and the ODIN for the recent period 2016-2022, since the GDB just offers data for one year and the ODB and the SCI cover earlier periods only. Similar to the slow evolution of state capacity (see, e.g., Glaeser _et al._ (2004) and Savoia and Sen (2019)), country statistical capacity typically takes time to build up and so would change gradually, rather than abruptly, under most normal circumstances (Ngaruko, 2008; Cameron _et al._ , 2021). Figure 3, the top left corner panel plots the scores in 2022 against those in 2016 for the two indexes. While the ODIN and the SPI has similar trends, the latter has somewhat stronger goodness-of-fit statistics (i.e., the R<sup>2</sup> equals 0.85 for the SPI and 0.58 for the ODIN). We further consider the standard deviation of the overall index for this period as another measure of index volatility. Overall, the 10
ODIN overall scores are more volatile. It has a global average standard deviation of 6, which is about 20 percent larger than that of 5.1 for the SPI overall score.

For further illustration, we randomly select 15 countries and plot the trend of their overall ODIN and SPI scores over time. While the trends appear similar for both tools, the ODIN scores show slightly more volatility. In particular, certain countries show abrupt changes of more than 17 percentage points for the ODIN scores such as Romania during 2017-2018, Serbia during 20162017, and Ukraine 2019-2020. The changes for the SPI overall score are much less, averaging 5 percentage points only for these three countries.

# _Relationship with key development outcomes_

Strong statistical capacity is well recognized as necessary for the functioning of the global sustainable development agenda, including creating and maintaining the infrastructure for monitoring SDG progress and generating the relevant data and indicators (Dang and Serajuddin, 2020; Barbier and Burgess, 2021; Bandona-Gill _et al._ , 2022). It would be useful to examine the relationship of the tools and the SDGs.

We plot in Figure 4 the bivariate correlation coefficients between the five tools (ODIN, ODB, GDB, SCI, and SPI) and a representative SDG indicator for each of the 17 SDGs and an SDG overall index generated as in Sachs _et al._ (2023), which reflects country progress in achieving SDG targets, with a higher score suggesting a stronger standing. We provide more detailed numbers in Appendix A, Table S6. The SPI shows the strongest correlation with most indicators (leading or tied in 10 out of 17 indicators), which is then followed by the GDB and the ODB (four indicators for each). The ODIN and the SCI each lead on one indicator only. For the overall SDG index, the SPI also shows the strongest correlation (0.82), to be followed by the GDB (0.74), the ODIN (0.72), the ODB (0.69), and the SCI (0.65). More careful tests of the correlations against each other are useful, and we show the t-tests to compare the correlations (i.e., the indexes shown in the smaller font in the right column under each index heading are not statistically significantly). For example, the SPI’s correlation with the overall SDG index is statistically different from those of the other tools. This is further supported with similar multivariate regression results shown in Appendix A, Tables S8 and S9.

Figure 5 similarly plots the correlations for the tools and six other commonly used development indexes, which include the Economics Complexity Index (Hartmann _et al._ , 2017), the 11
Environmental Performance Index (Wolf _et al._ , 2022), the OECD Better Life Index (Durand, 2015), the United Nations Human Development Index (UN, 2022), the World Bank Human Capital Index (Kraay, 2019), and the World Press Freedom Index (Reporters without Borders, 2023). The SPI has strongest correlation with three out of the six indexes, followed by the ODB (two indexes), and the ODB (one index). However, most of these correlation coefficients are not statistically significantly different from each other as shown by statistical tests and further multivariate regression results (Appendix A, Table S7 and Tables S10 to S21), suggesting the tools having rather similar relationships with these six development indexes.

# **Discussion**

We offer the first rigorous assessment of the various tools that are commonly used to assess country statistical capacity. These include the Global Data Barometer index (GDB), the Open Data Inventory index (ODIN), the Statistical Performance Indicators and Index (SPI), their predecessors (the ODB and the SCI), and some regional and self-assessment tools (including the IIAG). Each tool follows its own guiding principles in collecting data and generating its metrics. Compared to the SPI framework, the ODIN and the ODB focus more on data services and products, the IIAG and the SCI focus more on data products and infrastructure, and the GDB and all the three selfassessment tools focus more on data infrastructure. While the SPI (and SCI) obtain most of the data from curated international databases, the ODIN exclusively from NSO websites, the GDB (and ODB) mostly from expert surveys, the IIAG from a mix of different sources, and the three self-assessment tools from expert self-assessment tools. These different methods could offer their own strengths and weaknesses.

Except for the IIAG that focuses on Africa, all the remaining tools are global. The ODIN covers the most countries, to be followed by the SPI (and the SCI), the GDB (and the ODB), and the IIAG (54 countries during 2010-2019). Regarding measuring recent country progress over time, the ODIN and SPI are the two available choices, with the SPI showing somewhat less volatility.

Since strong statistical capacity is crucial for monitoring SDG progress and other key development outcomes, we examine their relationships with all the tools. For the overall SDG index, the SPI shows the strongest correlation. The SPI also exhibits stronger correlations with other popular development indexes such as the Economics Complexity Index, the OECD Better 12
Life Index, the United Nations Human Development Index, the World Bank Human Capital Index, the World Press Freedom Index, and Yale University’s Environmental Performance Index.

# **Potential mechanisms**

While the different components of each tool form an integral whole, the self-reinforcing five SPI pillars help showcase a virtuous data cycle (Figure A.1 in Dang _et al._ (2023)). For specific illustration, Figure 6 suggests a theory of change, where the five SPI pillars together result in greater data production transparency that enables more data investment and analysis capability and facilitates data flows to key users. The key users—which consist of the government, international organizations, civil society, academia, and the private sector—ensure greater data accountability, provide better policy making and service delivery, and obtain more business efficiency. A better functioning economy, in turns, can contribute more resources to strengthen the country’s statistical capacity. This whole process leads to better development outcomes. Conceptually, this process applies not just to the SPI, but other assessment tools as well. More generally, this theory of change is consistent with the economic theory of asymmetric information (information economics), whereby better information flows among different stakeholders in the economy (including firms, workers, investors, and traders) produce more economic benefits and enable the economy to operate more smoothly (Stiglitz, 2002).

This process is broadly consistent with the cumulating evidence from a growing literature in economics that investigates related data topics. These include the studies that analyze the SCI (Appendix A, Table S1). Other recent studies suggest that increased data transparency is found to have beneficial effects on GDP and economic growth (Arezki _et al._ , 2020; Islam and Lederman, 2020), external borrowing costs (Kubota and Zeufack, 2020) as well as a positive relationship with more democracy (Janus, 2022). More data dissemination, as measured by the amount of data that countries reported to the World Development Indicators (WDI) database maintained by the World Bank, could also help raise economic growth (Hodelin, 2022). The beneficial impacts of improved data transparency are also well-documented in other disciplines such as political science (e.g., Kelly and Simmons (2014)) and natural sciences (e.g., Nagaraj _et al._ (2020)). As a recent example, Canergie _et al._ (2021) observe that populist governments report less data to the WDI compared to non-populist governments.

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# **Political economy concerns**

However, a note of caution can be useful. Better statistics may work (more effectively) under certain conditions. Improving a country’s statistical capacity equips this country with a better tool, but perhaps what is more important is how these tools are used for the public good and ensuring that these tools are not misused. Put differently, improving a country’s statistical capacity should not be considered separately as an end by itself, but should be examined together with other political economy factors such as governance (including government incentives) to maximize its beneficial impacts. Indeed, some recent theoretical evidence suggests that improved economic statistics may even inhibit government reform attempts, since with better statistics, politicians no longer can hide if their reform efforts are failing and thus face a higher risk of electoral losses (Binswanger and Oechslin, 2020).

Alternatively, policy makers and politicians may fear public or timely data dissemination for a variety of reasons, such as more accountability, increased public scrutiny (e.g., the data may present a simplistic perspective that does not do full credit to the complex policy-making process) or potential loss of immediate public support (e.g., policies may take more time than expected to work) (Taylor, 2016; Dargent _et al._ , 2018; Agrawal and Kumar, 2020) or not being able to secure more loans (Coyle, 2015). Similarly, for sustainable solutions, improving statistical capacity may not just include setting things right in the short term, but may also require more nuanced thinking about the varying degrees that official economic indicators can be manipulated in specific contexts, and how solutions can be tailor-made to address these challenges on a longterm basis. This process is appropriately named “many shades of wrong” by Aragao and Linsi (2022).

# **Future directions**

More optimistically, the world is expected to be moving toward an integrated national data system (INDS) framework, which enables the production of data relevant to development, and fosters the equitable and safe flow of data between government, individuals, civil society, academia, and the private sector (World Bank, 2021). In this process, countries at different levels of data (or resources) development can formulate their own best strategy to improve their statistical capacity. Specifically, at the basic level, countries can prioritize establishing the fundamentals of a national data system. Once the fundamentals are in place, countries can seek to initiate better 14
data flows among different stakeholders. At advanced levels of data maturity, the goal is to optimize the system for the best possible outcomes.

Kitzmueller _et al._ (2021) offer a discussion of the data systems and efforts to improve them using three real-life country examples—Ghana, Mexico, and Estonia that correspond to the three levels of data development—and well illustrate each country’s current position regarding statistical capacity with their SPI score. Jolliffe _et al._ (2023) argue that data produced by the public sector can have transformational impacts on development outcomes through better targeting of resources, improved service delivery, cost savings, increased accountability, and more. Carpenter _et al._ (2022) and Reddy _et al._ (2023) describe various South-South and North-South initiatives to improve local statistical capacity (in medical statistics), ranging from setting up joint graduate study programs and research projects to consulting consortiums for rapid response to the COVID19 pandemic. Using volunteer data collected by other stakeholders, including citizen scientists, private sector companies, and other organizations, could encourage the public to further participate in building up statistical capacity, filling SDG data gaps, and enhancing government ability to monitor public service delivery and better protect the environment (Conrad and Hilchey, 2011; Meijer and Potjer, 2018; Fritz _et al._ , 2019).

Yet, while the amount of data produced by the public sector is increasing rapidly, the full potential of data to improve development outcomes has not been realized yet because of challenges of suboptimal data quality or lack of data on vulnerable groups. This limitation is most highlighted during times of crises. For example, recent studies on the COVID-19 pandemic suggest that vulnerable groups, including poorer households, women, children, and refugees, were most affected (Dang and Nguyen, 2021; Sumner et al., 2022; Li _et al._ , 2023). In this regard, the importance of measurement of statistical capacity has been recognized in the SDG indicator framework with the recent formal population of SDG indicator 17.18.1 with the SPI pillars 4 and 5 indicators and the ODIN data availability indicators is a step in the right direction.<sup>2</sup> Since better statistical capacity could help collect more data, such as on gender-disaggregated data and climate change, Tichenor (2022) argues that assessment tools such as the SPI can be regarded both as a tool for monitoring development and as a development goal itself.

> 2 SDG 17.18.1 is made up of one ODIN indicator (component a) and two SPI indicators (components b and c).

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Besides the approaches to improving country statistical capacity discussed above, a promising direction to address these challenges is to operationalize these tools using a bottom-up approach. For instance, there have been ongoing efforts at the World Bank to include SPI indicators at the project level as specific metrics for monitoring statistical capacity, especially for low-income countries in Sub-Saharan Africa. This setting is similar to the other metrics that have been employed to measure project performance in other development projects. The SPI framework is flexible enough to allow either direct application of all its indicators, some of its indicators, or even some re-weighted version of its indicators to better suit the specific country context. Another direction is to “collect more data about data”, that is, implementing institutional surveys to directly collect data from NSOs to measure progress with their statistical capacity. These surveys can offer supplementary information to enrich the current indicators and, if designed appropriately, may help shed more light on the country-specific political economy environment regarding statistical capacity.

# **Materials and Method**

# **Conceptual motivations and construction of the SPI**

# _Conceptual motivations_

The SPI is built on a framework that is forward looking, measures less mature statistical systems as well as advanced systems, covers a country’s entire NSS (rather than just the NSO as with some previous indexes), and provides countries with incentives to build a modern statistical system. In particular, by helping countries and development partners identify the strengths and weaknesses of NSSs, the SPI can support policy advice to improve or benchmark NSSs, offer advocacy for national statistics, and facilitate investment decisions for governments and (bilateral and multilateral) donors. The SPI is also built on standard _desiderata_ for a statistical index (i.e., simple, coherent, motivated, rigorous, implementable, replicable, incentive consistent), as well as clear conceptual and mathematical foundations. Importantly, the SPI is also open-data and opencode where users can freely access data and experiment with different adjustments to the index on the World Bank’s website. (We return to more discussion on data access in the next section.) It is useful to note that while measuring a country’s statistical _capacity_ is our ultimate goal, this task is difficult, if not impossible to implement at scale for all countries, given the typically unobserved inherent characteristics with an NSS. It is, however, relatively more straightforward 16
to measure a country’s statistical _performance_ through objective and comparable indicators. (This challenge is highlighted by a large number of indicators with missing data that we discuss later. Also see Cameron _et al._ (2021) for further discussion on this and the _desiderata_ .) We identify five key pillars of a country’s statistical performance, as shown in Figure 7. These are data use, data services, data products, data sources, and data infrastructure, which can be further disaggregated into 22 dimensions. This figure shows these pillars and dimensions in the form of a dashboard, which can help countries identify areas for development in their statistical system. We briefly describe these pillars below and provide more details on the dimensions of the SPI, including ongoing data work, in Appendix A, Table S1.

Since statistics have no value unless they are used, the first pillar of the SPI is data use. In order to meet user needs, the statistical system must develop a range of services that connect data users and producers and facilitate dialogue between them. The second pillar of the SPI is therefore data services that are trusted by users. The dialogue between users and suppliers in turn drives the design of statistical products that are to be created including the quality of product needed for the country requirement. This will incorporate accuracy, timeliness, frequency, comparability, and levels of disaggregation. The third pillar of the SPI is therefore data products. In order to create the products required, the statistical system needs to make use of a variety of sources from both inside and outside the government. This includes making use of typical data collection methods like censuses and surveys, but also administrative data, geospatial data, and data generated from the private sector and from citizens. The fourth pillar of the SPI is therefore data sources. For the cycle to be complete, capability needs continuously to be reviewed to ensure that it is enough to deliver the products, services and ultimately data use required. The fifth pillar of the SPI is therefore data infrastructure.

In summary, a successful statistical system offers highly valued and well-used statistical services, generates high quality statistical indicators that can also track progress for the SDGs, draws on all types of data sources relevant to the indicators that are to be produced, develops both hard infrastructure (including legislation, governance, standards) and soft infrastructure (including skills, partnerships), and has the financial resources to deliver. Figure A.1 (Dang _et al._ , 2023) offers an alternative visual description of the beneficial interactions of the different data pillars, which reinforce each other through stakeholders’ partnership, joint accountability, better capacity, 17
and meeting user needs. Improvements in performance can be represented as a virtuous data cycle that can become self-sustaining.

# _Further description of SPI pillars and dimension_

A quick primer on names. We refer to the 5 rows in the framework in Figure 7 as pillars. We refer to the 22 cells in the framework in Figure 7 as dimensions. Finally, each dimension may be composed of multiple indicators. For instance, the dimension on censuses and surveys is made up of indicators on whether population censuses have been conducted, agriculture censuses, labor force surveys, etc.

# **Data use**

The data use pillar is segmented by user type. The tiles on the Dashboard provide an indicator of use of statistics respectively by the legislature, executive, civil society (including sub-national actors), academia and international bodies. A mature system would score well across the tiles. Areas for development would be highlighted by weaker scores in that domain enabling questions to be asked about prioritization among user groups and why existing services are not resulting in higher use of national statistics in that segment.

# **Data services**

The data services pillar is segmented by service type. The tiles on the Dashboard provide an indicator of the quality of data releases, the richness and openness of online access, the effectiveness of advisory and analytical services related to statistics and the availability and use of data access services such as secure microdata access. Advisory and analytical services might incorporate elements related to data stewardship services including ethical consideration of proposals and calling out misuse of data in accordance with the Fundamental Principles of Official Statistics.

# **Data products**

The data products pillar is segmented by topic and organized into social, economic, environmental and institutional domains using the typology of the Sustainable Development Goals. This approach enables comparisons across countries and anchors the system in the 2030 agenda so that a global view can be generated while enabling different emphasis to be applied in different countries to reflect the user needs of that country.

# **Data sources**

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The data sources pillar is segmented between sources generated by the statistical office (censuses and surveys) and sources accessed from elsewhere (administrative data, geospatial data, private sector data and citizen generated data). The appropriate balance between these types of sources will vary depending on the institutional setting and maturity of the statistical system in each country. High scores should reflect the extent to which the sources being utilized enable the necessary statistical indicators to be generated. For example, a low score on environment statistics may reflect a lack of use of (and low score for) geospatial data. This linkage, which is inherent in the data cycle approach, should help highlight areas for investment if country needs are to be met.

# **Data infrastructure**

The data infrastructure pillar is segmented into hard and soft infrastructure segments itemizing essential cross-cutting requirements for an effective statistical system. The segments are:

1. Legislation and governance covering the existence of laws and a functioning institutional framework for the statistical system

2. Standards and methods addressing compliance with recognized frameworks and concepts 3. Skills including level of skills within the statistical system and among users (statistical literacy) 4. Partnerships reflecting the need for the statistical system to be inclusive and coherent finance, both domestically and from donors

# _Construction of the SPI_

We employ Cameron _et al._ ’s (2021) nested weighting structure to construct the SPI overall score. Compared to other weighting schemes, this weighting structure offers properties such as symmetry, monotonicity, and subgroup decomposability. It is based on Atkinson’s (2003) counting method, which was employed to construct a social exclusion index (Chakravarty and D’Ambrosio, 2006) and to measure adjusted multi-dimensional poverty (Alkire and Foster, 2011). Our statistical performance indicators have a three-level structure, and the SPI overall score is formed by sequentially aggregating the indicators at each level.

19
dimension is obtained by taking the unweighted average of all indicators in this dimension, including the indicators for the system of national accounts in use, national accounts base year, classification of national industry, CPI base year, and classification of household consumption (Appendix A, Table S1).

A score for each pillar is subsequently computed as the average score of the dimensions in that pillar. For pillars 1, 2, 4, and 5, the unweighted average of the dimensions within each pillar is taken. For pillar 3 on data products, we take a weighted average of the dimensions, where the weights are based on the number of SDGs in each dimension (6 SDGs in dimension 3.1 on social statistics, 6 SDGs in dimension 3.2 on economic statistics, 2 in dimension 3.3 on environmental statistics, and 2 in dimension 3.4 on institutional statistics. SDG 14 - Life Below Water - is omitted because land-locked countries do not report on these indicators.). This reflects a perspective that all SDGs are of equal importance, and therefore the dimensions are weighted accordingly. Additionally, for Pillar 4 on data sources, censuses and surveys are given separate weights, so that censuses, surveys, administrative data, and geospatial data each receives a weight of 1/4. While censuses and surveys are in the same pillar in the framework, and therefore each would typically only receive a weight of 1/6 in this dimension, because of their importance in producing many indicators, they are given extra weight such that each gets a weight of 1/4. (Using a weight of 1/6 for censuses and surveys provides very similar results. In particular, the correlation between the SPI overall score under the preferred approach and the alternative approach is 0.998.) The score for each pillar (S c ) is calculated as follows SS. S LL cc S c = ∑ <u>c</u> ×S c <u>c</u> (2) IIcc ωω SS.DDSSMM c is the weight for dimension _d_ SS. S in pillar LL cc _p_ , and cc=1 is the number of dimensions in pillar IIcc _p_ . Finally, the SPI overall score for country _c_ in time cc _t_ is derived by taking the average across the ωω II 5 pillars. The SPI overall score has a maximum score of 100 and a minimum of 0. A score of 100 would indicate that a country has every single element that we measure in place. A score of 0 indicates that none is in place. The SPI overall score (S c ) is calculated as follows S c = ∑ S SS. SSIIDDIIXXc <u>c</u> (3) IIcc SS.S LL where S c is the SPI pillar scores for country SS. SSIIDDIIXX cc=1 _c_ in time IIcc _t_ for the five pillars discussed above, and is the number of pillars. cc SS. S LL cc II 20
Dang _et al._ (2023) show that while all SPI pillars are positively correlated with one another, no pillar is perfectly correlated with any of the other pillars, indicating that each pillar provides additional information on a country’s statistical performance. Further decomposing the SPI into the contributions from each pillar suggests that in low income countries, adequate data sources (Pillar 4) and data infrastructure (Pillar 5) represent severe capacity limitation but high-income countries are doing relatively poorly in terms of data products. Overall, countries with a higher income level or a non-fragile-and-conflict status have a higher SPI score. We provide detailed discussion on the data collection process for the SPI indicators and other related challenges (e.g., potential issues with missing data) in Dang _et al._ (2023). The SPI is publicly available at <u>www.worldbank.org/spi. The associated code and underlying raw data are available at our project</u> site https://github.com/worldbank/SPI.

# **Statistical analysis: Country fixed-effects regressions**

As a start, we estimate the following pooled OLS model 0 1S (4) where represents the outcome variable for country cc,cc cc,cc cc,cc in year , which can be either GDP per YY = αα + αα SS + ηη capita (in logarithmic form based on 2015 constant US dollars) or the WGI. cc,cc is an idiosyncratic YY cc tt error term. S is the value of the country overall SPI score in year . Furthermore, we also cc,cc ηη examine another model specification where we replace the overall SPI score with the five SPI cc,cc SS tt pillar scores (on data use, data services, data products, data sources, and data infrastructure). This disaggregation allows us to probe more deeply into the relationship of the outcome variable and the different SPI components.

While Equation (4) offers a useful exploratory analysis, it does not control for country-specific or year-specific characteristics that can affect the outcome variables. These can include, for example, country income or education levels or the structure of its economy, or global macroeconomic time trends. Consequently, we further estimate a panel data model with country and year fixed effects (FE): 0 1SS (5) where is the country fixed effects, cc,cccc,cc is a year dummy variable. We also estimate the following cc,cccc,cc cc cc cc,cc YY = ββ + ββ SS + ππ + ττ + εε 0 1SS (5) where is the country fixed effects, cc,cccc,cc is a year dummy variable. We also estimate the following cc,cccc,cc cc cc cc,cc country and year FE panel data model where we explicitly control for several country cc cc ππ ττ characteristics: 21
0 1S (6) Equation (6) adds to Equation (5) a vector of control variables cc,cc cc,cc cc,cc cc cc cc,cc , which includes the added YY = γγ + γγ SS + γγ<sup>′</sup> XX + θθ + σσ + εε values in manufacturing, agriculture, forestry, and fishing, and trade values (all as shares of cc,cc XX country GDP), which are measures of the sectoral composition of the economy. All these three variables are measured as a percentage of the country GDP. also includes the gross primary school enrollment rate. further includes either an index of political institutions as measured by cc,cc XX WGI (when the outcome variable is log of GDP per capita) or country income levels as measured cc,cc XX by log of GDP per capita (when the outcome variable is WGI).

Since we do not have all the country-year observations for these control variables (except for the WGI), we impute for the missing observations with the nearest available values. The percentage of missing values ranges from 1 percent (agriculture, forestry, and fishing added values as a share of GDP) to more than 25 percent (gross primary school enrollment). We focus on the resulting balanced panel data for 159 countries with data for the SPI, WGI, and other control variables between 2016 and 2022. The main reason that most countries do not have an overall SPI score in 2016 is due to data unavailability from Open Data Watch’s Open Data Inventory (ODIN), which was used for the SPI measures of data openness and geospatial information. The other reasons are missing human capital index scores or trade data. As such, Equation (5) is our preferred model for analysis but Equation (6) can offer useful robustness checks.

It is important to emphasize that these econometric models are unlikely to allow us to identify the causal impacts of the SPI on GDP growth or governance (which is beyond the scope of analysis in this paper). Yet, these models can help shed exploratory, useful insights on the correlational relationship between a country’s statistical performance and its economic growth and governance.

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**Table 1. Comparing the SPI and Other Data and Statistics Indexes**

|||**Assessment Type/**|**Country**|**Time**|**Index**|**No. of**|||**Share of i**<br>|**ndicators in**<br>|**eachpil**<br>|**lar**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**No**|**Index**|<br>**Frequency**|<br>**Coverage**|**Coverage**|**Methodology**|**Indicators**|**Data**<br>**Use**|**Data**<br>**Services**|<br>**Data**<br>**Products**|<br>**Data**<br>**Sources**|**Data**<br>**Infra.**|**Data Sources (Share)**|
|1|Open Data Inventory<br>Indicators & Index (ODIN)|Global/ Annual|192|2016-2022|Nested weight|44|0%|50%|50%|0%|0%|NSO Websites|
|2|Open Data Barometer<br>(ODB)|Global/ Annual|116|2013-2017|Nested weight|46|15%|30%|37%|0%|15%|Expert Survey|
|3|Global Data Barometer<br>(GDB)|Global/ Biennial|109|2019-2021|Nested weight|55|9%|4%|20%|16%|45%|Expert Survey|
|4|Statistical Performance<br>Indicators & Index (SPI)|Global/ Annual|186|2016-2022|Nested weight|51|10%|16%|31%|20%|24%|Public International<br>Databases (86%), NSO<br>Websites (14%)|
|5|Statistical Capacity<br>Indicators & Index (SCI)|Global/ Annual|145|2004-2020|Simple<br>arithmetic<br>average|25|0%|0%|40%|20%|40%|Public International<br>Databases (80%), NSO<br>Websites (20%)|
|6|Ibrahim Index of African<br>Governance Statistical<br>Capacity (IIAG)|Regional/ Annual|54|2010-2022|Simple weight|3<br>statistical<br>capacity<br>indicators|0%|4%|37%|19%|41%|Expert Survey (33%),<br>ODIN (33%), SCI<br>databases (33%)|
|7|EU Snapshot tool|Self-assessment/<br>N.A.|N.A.|N.A.|N.A.|131|6%|12%|10%|8%|64%|Expert self-<br>assessment|
|8|UN NQAF self-checklist|Self-assessment/<br>N.A.|N.A.|N.A.|N.A.|87|10%|16%|2%|7%|64%|Expert self-<br>assessment|
|9|Paris21 NSDS self-<br>assessment|Self-assessment/<br>N.A.|N.A.|N.A.|N.A.|149|14%|23%|1%|1%|61%|Expert self-<br>assessment|

**Note** : N.A. stands for not available. The number of countries shown under the column “Country coverage” are for the latest year for all the indexes. The SPI covers between 167 and 186 countries for 2016-2022. The SCI covers between 115 and 145 countries for 2004-2020. The ODIN covers between 125 and 192 countries for 2015-2022 but offers comparable data starting from 2016. The ODB covers between 30 and 115 countries for 2013-2017. The ODB only covers 30 countries in 2017 and is replaced by the GDB after this year. The GDB covers 109 countries for 2019-2021. The IIAG covers 54 countries for all the years for 2010-2019. All the indexes provide annual data, except for the GDB that provides a single data point for each country for 2019-2021.

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# **Appendix A. Additional Tables**

# **Table S1. An Overview of the World Bank’s Statistical Capacity Index (SCI) in Selected Recent Studies**

|1|Angrist, Goldberg &<br>Jolliffe (2021)|_Journal of Economic_<br>_Perspective_|Global analysis|Measuring economic growth in<br>developing countries|Poorer countries have lower statistical capacity,<br>which can severely bias their reported<br>measurements of economic growth.|
|---|---|---|---|---|---|
|2|Anderson & Whitford<br>(2017)|_Review of Policy_<br>_Research_|100 countries|Technological attainment and<br>statistical capacity|Countries with greater levels of technological<br>attainment have greater national statistical<br>capacity.|
|3|Goren & Winkler (2022)|_Journal of African_<br>_Economies_|57 African<br>countries|Low-quality statistics, slave trades<br>and development|Replacing mismeasured GDP per capita by<br>nighttime light intensity per capita significantly<br>reduces the impact of the slave trade on<br>economic development by a factor of 2 to 4.|
|4|Hanson & Sigman (2021)|_Journal of Politics_|139 countries|Measuring state capacity in<br>political science research|The SCI is most strongly correlated with state<br>capacity compared to other indicators in<br>bureaucratic quality, public administration, law<br>and order ratings, or state fiscal capacity.|
|5|Henderson, Storeygard &<br>Weil (2012)|_American Economic_<br>_Review_|113 countries|Better measuring income growth<br>with night lights data|SCI can help provide more accurate estimates of<br>country income growth.|
|6|Hu & Yao (2022)|_Journal of_<br>_Econometrics_|162 countries|Estimating the relationship<br>between nighttime light growth<br>and GDP growth|SCI can help provide more accurate estimates of<br>country GDP growth.|
|7|Jacob (2017)|_World Development_|145 countries|Impact of data gaps on<br>Millennium Development Goals<br>achievement (MDG)|Stronger country statistical capacity increases the<br>probability of MDG success.|

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|8|Martinez (2022)|_Journal of Political_<br>_Economy_|137 countries|Autocracies overstate yearly GDP<br>growth|Limitations in country statistical capacity do not<br>significantly affect autocracies' exaggeration of<br>GDP growth.|
|---|---|---|---|---|---|
|9|Oechslin & Steiner (2022)|_Review of_<br>_International_<br>_Organization_|146 countries|Statistical capacity and corrupt<br>bureaucracies|A positive relationship between the<br>growth rate of real GDP per capita and statistical<br>capacity exists for countries with low corruption,<br>but not for countries with high corruption.|
|10|Sanderfur & Glassman<br>(2015)|_Journal of_<br>_Development Studies_|Sub-Saharan<br>African countries|Political economy of bad data|Sub-Saharan African countries as a whole have a<br>lower SCI score (i.e., 58) than the global average<br>(i.e., 64), but much heterogeneity exists with<br>country scores ranging from the bottom to more<br>than the 75th global percentile.|
|11|Sanga et al. (2011)|_International_<br>_Statistical Review_|43 African<br>countries|Proposing an index to measure<br>statistical capacity for African<br>countries|The SCI does not cover certain aspects of an<br>NSO such as organization, human development,<br>and funding. There is a weak correlation<br>between the SCI and the proposed index.|
|12|Tapsoba et al. (2017)|_Journal of_<br>_International_<br>_Development_|62 developing<br>countries|Statistical capacity building<br>impacts on reducing procyclical<br>fiscal policy|IMF-supported technical analysis to countries<br>improves their statistical capacity during 1990-<br>2012.|

**Note** : SCI stands for statistical capacity index.

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# **Table S2. Description of SPI Dimensions**

|Dimension|Brief Description|
|---|---|
|Dimension 1.1: Data<br>use by national<br>legislature|Not included because of lack of established methodology. In principle it may be<br>possible to utilize websites of national legislatures but this will require further work and<br>assessment.|
|Dimension 1.2: Data<br>use by national<br>executive branch|Not included because of lack of established methodology. There are some usable data<br>sources (as used by (PARIS21 2019)) but gaps in data across countries have prevented<br>full adoption.|
|Dimension 1.3: Data<br>use by civil society|Not included because of lack of established methodology. There are some usable data<br>sources with good coverage, for example from social media but more data is required to<br>help assess and allow for likely biases between and within countries.|
|Dimension 1.4: Data<br>use by academia|Not included because of lack of established methodology. We have not been able to<br>find usable data sources with global coverage on which a new methodology could be<br>developed.<br>Five measures usefulness or reliability of country produced measures for international<br>organizations have been included. First, on comparability of poverty estimates for the<br>World Bank reporting on international poverty usingthe Poverty and Inequality Portal|
|Dimension 1.5: Data<br>use by international<br>organizations|(PIP). Second on usable surveys for statistics on child mortality for theUN Inter-<br>agency Group for Child Mortality Estimation. Third on accuracy of debt reporting as<br>classified by the World Bank (Source: World Bank WDI metadata). Fourth, on<br>availability of safely managed drinking water data for use byWHO/UNICEF Joint<br>Monitoring Programme.Fifth, on labor force participation data for use by ILO. While<br>these data sources provide only a partial coverage of data used by international<br>organizations, they do provide an indication of the performance of the national<br>statistical system.|
|Dimension 2.1: Data<br>releases|SDDS/e-GDDS subscription. This indicator is based on whether the country subscribes<br>to IMF SDDS+, SDDS, or e-GDDS standards. The source is the IMF Dissemination<br>Standards Bulletin Board. This is a reliable data source but we recognize that it is a<br>proxy for the concept we are seeking to capture rather than a direct measurement.|
|Dimension 2.2: Online<br>access|ODIN Open Data Openness score (Jamison Crowell et al. n.d.). This is a well-<br>established data source with good country coverage, which scores countries based on<br>whether indicators are available online in a format that is machine readable, in a non-<br>proprietary format, downloadable, with metadata available and terms of use. Scores<br>range from 0-1. For more details, consult theODIN technical documentation|
|Dimension 2.3:<br>Advisory/ Analytical<br>Services|Not included because of lack of established methodology. This could be a new<br>indicator of the number of non-recurring products on NSO website (ad<br>hoc/experimental rather than regular releases). The indicator is the number of products<br>found. No established source exists for this indicator.|
|Dimension 2.4: Data<br>access services|NADA metadata. This indicator checks whether NADA microdata cataloging is<br>available for surveys produced by NSO. NADA is an open source microdata cataloging<br>system, compliant with the Data Documentation Initiative (DDI) and Dublin Cores<br>RDF metadata standards. Source: NSO websites.|

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|Dimension|Brief Description|
|---|---|
||Availability of Goal 1-6 indicators, measured by an average score. The primary data<br>source is the UN SDG database. While this is a database with comprehensive coverage<br>that all countries have signed up to, many countries are not yet submitting all their|
|Dimension 3.1: social<br>statistics|<br>available national data. Scores for some countries thus may not capture their<br>performance in calculating the indicators. For OECD countries, we supplement the UN<br>SDG database with comparable data submitted to the OECD following the<br>methodology inMeasuring Distance to the SDG Targets 2019: An Assessment of<br>Where OECD Countries Stand.|
|Dimension 3.2:<br>economic statistics|Availability of Goal 7-12 indicators, measured by an average score. See 3.1.|
|Dimension 3.3:<br>environmental statistics|Availability of Goal 13 & 15 indicators, measured by an average score. Goal 14 - Life<br>on Water - is not included because land-locked countries do not report on these<br>indicators. See 3.1.|
|Dimension 3.4:<br>institutional statistics|Availability of Goal 16-17 indicators measured by an average score. See 3.1.|
||Availability of recent censuses and surveys covering broad areas. The following<br>censuses and surveys are considered: Population & Housing census, Agriculture|
|Dimension 4.1:<br>censuses and surveys|<br>census, Business/establishment census, Household Survey on income/ consumption/<br>expenditure/ budget/ Integrated Survey, Agriculture survey, Labor Force Survey,<br>Health/Demographic survey, Business/establishment survey. Source: NSO websites,<br>World Bank microdata library, ILO microdata library, IHSN microdata library|
||Availability of Civil Registration and Vital Statistics (CRVS) indicator. An ideal<br>indicator would include a score based on the density of administrative data available in<br>sectors of social protection, education, labor, and health. However, social protection,|
|Dimension 4.2:<br>administrative data|<br>education, health, and labor admin data indicators not included because of lack of<br>established methodology. While several promising sources for administrative data from<br>the World Bank’s ASPIRE team, WHO, UNESCO, and ILO have been identified, the<br>were not included due to incomplete coverage across countries. Further research and<br>data collection effort would be needed to fill in this information, so that a more<br>comprehensive picture of administrative data availability can be produced.|
||Geospatial data available at 1st Admin Level. This data source from Open Data Watch<br>|
||focusing on data availability at the sub-national level provides a partial understanding|
|Dimension 4.3:<br>geospatial data|<br>of a country’s ability to produce geospatial data. A research and data collection effort is<br>needed to develop an more comprehensive global database of the availability of key<br>geospatial indicators.|
|Dimension 4.4:<br>Private/citizen<br>generated data|Not included because of lack of established methodology. Currently no comprehensive<br>source exists to measure the use of private and citizen generated data in national<br>statistical systems, and this should be another area where more data collection is<br>needed by the international community.|
||This indicator is based on PARIS21 indicators on SDG 17.18.2 (national statistical|
|Dimension 5.1:<br>Legislation and|<br>legislation compliance with UN Fundamental Principles of Official Statistics),<br>existence of National Statistical Council, national statistical strategy generation,|
|governance|national statistical plan. Limited country coverage makes cross country comparison<br>limited. So this is included in the dashboard, but not in the overall SPI score or index.|

40
|Dimension|Brief Description|
|---|---|
||This set of indicators is based on countries’ use of internationally accepted and<br>recommended methodologies, classifications and standards regarding data integration.<br>These indicators help facilitate data exchange and provide the foundation for the<br>preparation of relevant statistical indicators. The following methods and standards are<br>considered: System of national accounts in use, National Accounts base year,|
|Dimension 5.2:<br>Standards and Methods|Classification of national industry, CPI base year, Classification of household<br>consumption, Classification of status of employment, Central government accounting<br>status, Compilation of government finance statistics, Compilation of monetary and<br>financial statistics, Business process. Further work could improve the validity of this<br>indicator and reduce the risk that countries may be incentivized to adopt only<br>traditional standards and methods and neglect innovative solutions that may be more<br>valid in the current context.|
||Not included because of lack of established methodology or suitable data sources. A|
|Dimension 5.3: Skills|new indicator drawing on PARIS21 indicators such as statistical society presence and<br>data literacy could be developed and is an area of future work.|
||Not included because of lack of established methodology or suitable data sources. A|
|Dimension 5.4:|new indicator based on textual analysis of NSS reports/websites for references to|
|Partnerships|partner organizations could be developed. This is an area of future work.|
||The indicator is based on PARIS21 SDG indicators (SDG 17.18.3 (national statistical|
|Dimension 5.5: Finance|plan that is fully funded and under implementation). It is included in dashboard, but not<br>in the overall SPI score or index because of insufficient country coverage.|

**Table S3. Mapping of SPI Indicators to SDG Indicators**

||**SPI Indicator**|**SPI**<br>**Dimension**|**SDG**|**Data Source**|
|---|---|---|---|---|
|**1**|Availability of Comparable Poverty<br>headcount ratio at $1.90 a day (5 year<br>moving average of availability)|Dimension<br>1.5: Data use<br>by<br>international<br>organizations|SDG<br>1.1.1|World Bank's PIP|
|**2**|Availability of Mortality rate, under-5<br>(per 1,000 live births) data meeting<br>quality standards according to UN<br>IGME (5 year moving average of<br>availability)|Dimension<br>1.5: Data use<br>by<br>international<br>organizations|SDG<br>3.2.1|Child Mortality Metadata<br>from UN IGME|
|**3**|Quality of Debt service data according<br>to World Bank|Dimension<br>1.5: Data use<br>by<br>international<br>organizations|SDG<br>17.4.1|Debt Reporting Metadata<br>from World Bank|
|**4**|Safely Managed Drinking Water|Dimension<br>1.5: Data use<br>by<br>international<br>organizations|SDG<br>6.1.1|Availability of Safely<br>Managed Drinking Water<br>data for use by JMP|
|**5**|GOAL 1: No Poverty (5 year moving<br>average of availability)|Dimension<br>3.1: Social<br>(SDG 1-6)|SDG 1|UN Global SDG Indicators<br>Database|

41
|**6**|GOAL 2: Zero Hunger (5 year moving<br>average of availability)|Dimension<br>3.1: Social<br>(SDG 1-6)|SDG 2|UN Global SDG Indicators<br>Database|
|---|---|---|---|---|
|**7**|GOAL 3: Good Health and Well-being<br>(5 year moving average of availability)|<br>Dimension<br>3.1: Social<br>(SDG 1-6)|SDG 3|UN Global SDG Indicators<br>Database|
|**8**|GOAL 4: Quality Education (5 year<br>moving average of availability)|Dimension<br>3.1: Social<br>(SDG 1-6)|SDG 4|UN Global SDG Indicators<br>Database|
|**9**|GOAL 5: Gender Equality (5 year<br>moving average of availability)|<br>Dimension<br>3.1: Social<br>(SDG 1-6)|SDG 5|UN Global SDG Indicators<br>Database|
|**10**|GOAL 6: Clean Water and Sanitation<br>(5 year moving average of availability)|<br>Dimension<br>3.1: Social<br>(SDG 1-6)|SDG 6|UN Global SDG Indicators<br>Database|
|**11**|GOAL 7: Affordable and Clean Energy<br>(5 year moving average of availability)|<br>Dimension<br>3.2: Economic<br>(SDG 7-12)|SDG 7|UN Global SDG Indicators<br>Database|
|**12**|GOAL 8: Decent Work and Economic<br>Growth (5 year moving average of<br>availability)|<br>Dimension<br>3.2: Economic<br>(SDG 7-12)|SDG 8|UN Global SDG Indicators<br>Database|
|**13**|<br>GOAL 9: Industry, Innovation and<br>Infrastructure (5 year moving average<br>of availability)|<br>Dimension<br>3.2: Economic<br>(SDG 7-12)|SDG 9|UN Global SDG Indicators<br>Database|
|**14**|<br>GOAL 10: Reduced Inequality (5 year<br>moving average of availability)|<br>Dimension<br>3.2: Economic<br>(SDG 7-12)|SDG 10|UN Global SDG Indicators<br>Database|
|**15**|GOAL 11: Sustainable Cities and<br>Communities (5 year moving average<br>of availability)|<br>Dimension<br>3.2: Economic<br>(SDG 7-12)|SDG 11|UN Global SDG Indicators<br>Database|
|**16**|<br>GOAL 12: Responsible Consumption<br>and Production (5 year moving<br>average of availability)|<br>Dimension<br>3.2: Economic<br>(SDG 7-12)|SDG 12|UN Global SDG Indicators<br>Database|
|**17**|<br>GOAL 13: Climate Action (5 year<br>moving average of availability)|<br>Dimension<br>3.3:<br>Environmental<br>(SDG 1315)|SDG 13|UN Global SDG Indicators<br>Database|
|**18**|GOAL 15: Life on Land (5 year moving<br>average of availability)|,<br>Dimension<br>3.3:<br>Environmental<br>(SDG 1315)|SDG 15|UN Global SDG Indicators<br>Database|
|**19**|GOAL 16: Peace and Justice Strong<br>Institutions (5 year moving average of<br>availability)|,<br>Dimension<br>3.4:<br>Instituational<br>(SDG 16-17)|SDG 16|UN Global SDG Indicators<br>Database|
|**20**|GOAL 17: Partnerships to achieve the<br>Goal (5 year moving average of<br>availability)|<br>Dimension<br>3.4:<br>Instituational<br>(SDG 16-17)|SDG 17|UN Global SDG Indicators<br>Database|
|**21**|Legislation and governance|<br>Dimension<br>5.1:<br>Legislation<br>and<br>governance|SDG<br>17.18.2|UN Global SDG Indicators<br>Database|

42
# **Table S4. SPI overall score and Pillar Scores in 2022**

Below, the full list of countries by their SPI overall score in 2022 is presented. The first column is the country name and the following columns are the overall SPI overall score, and then the subscores for pillars 1, 2, 3, 4, and 5.

The purpose of the SPI is to help countries assess and improve the performance of their statistical systems. The presentation of SPI overall scores is designed to reflect that aim. Small differences between countries should not be stressed since they can reflect imprecision arising from the currently available indicators rather than meaningful differences in performance. Instead, the presentation of overall SPI scores focuses on larger groupings of countries reflecting broad categories of performance as measured by the indicator framework. In total there are 186 countries with sufficient data to compute an index value. This set of countries covers 99.3 percent of the world population.

Countries shaded in dark orange are the lowest performing, countries in dark green are the highest performing. Countries are grouped into five groups:

1. **Top Quintile** : Countries in the top 20% are classified in this group. Shading in dark green.

2. **4th Quintile** : Countries in the 4th quantile, or those above the 60th percentile but below the 80th percentile are in this group. Shading in light green.

3. **3rd Quintile** : Countries in the 3rd quantile, or those between the 40th and 60th percentile, are classified in this group. Shading in yellow.

4. **2nd Quintile** : Countries in the 2nd quantile, or those above the 20th percentile but below the 40th percentile, are in this group. Shading in light orange.

5. **Bottom 20%** : Countries in the bottom 20% are classified in this group. Shading in dark orange .

|Economy|SPI<br>overall<br>score|Pillar 1:<br>Data Use|Pillar 2: Data<br>Services|Pillar 3: Data<br>Products|Pillar 4: Data<br>Sources|Pillar 5: Data<br>Infrastructure|
|---|---|---|---|---|---|---|
|Finland|93.6|100.0|96.4|88.5|83.3|100|
|Norway|93.5|100.0|97.1|87.2|83.1|100|
|Canada|92.9|100.0|92.6|83.7|88.3|100|
|Netherlands|92.8|100.0|96.9|87.8|79.5|100|
|United States|92.8|100.0|93.6|86.0|84.4|100|
|Slovenia|92.5|100.0|97.5|87.1|78.1|100|
|Sweden|92.2|100.0|96.0|86.4|78.7|100|
|Italy|91.9|100.0|93.0|88.7|77.8|100|
|Denmark|91.6|90.0|98.7|86.5|82.9|100|
|Poland|91.6|90.0|97.1|86.8|84.0|100|
|Spain|91.4|100.0|91.1|82.9|83.1|100|

44
|Economy|SPI<br>overall<br>score|Pillar 1:<br>Data Use|Pillar 2: Data<br>Services|Pillar 3: Data<br>Products|Pillar 4: Data<br>Sources|Pillar 5: Data<br>Infrastructure|
|---|---|---|---|---|---|---|
|Ireland|91.3|100.0|96.4|87.2|72.9|100|
|Germany|91.0|100.0|94.9|85.0|80.1|95|
|Czechia|90.9|100.0|88.8|84.4|81.2|100|
|France|90.8|100.0|92.1|86.2|75.6|100|
|Georgia|90.7|100.0|92.0|91.5|79.9|90|
|Austria|90.0|100.0|89.5|88.6|76.8|95|
|Australia|89.9|90.0|92.9|83.0|83.9|100|
|Costa Rica|89.9|100.0|86.3|93.1|80.2|90|
|Japan|89.9|100.0|90.3|84.9|79.2|95|
|Estonia|89.6|90.0|96.9|83.9|77.0|100|
|Portugal|89.3|90.0|93.1|87.4|76.1|100|
|Slovak Republic|89.1|90.0|94.5|85.3|76.0|100|
|Belgium|88.9|100.0|86.9|81.3|76.4|100|
|Latvia|88.8|100.0|97.1|76.1|70.9|100|
|Switzerland|88.8|100.0|88.4|85.6|80.0|90|
|Greece|88.7|100.0|88.1|78.6|77.0|100|
|New Zealand|88.7|100.0|92.4|82.9|78.4|90|
|Mexico|88.6|100.0|93.4|93.0|81.5|75|
|Lithuania|88.1|90.0|91.1|82.3|77.2|100|
|Hungary|87.9|100.0|89.0|88.2|72.3|90|
|Korea, Rep.|87.8|100.0|92.1|83.0|79.2|85|
|Luxembourg|87.8|100.0|93.4|81.7|64.1|100|
|Turkiye|87.7|100.0|86.8|94.2|57.6|100|
|Chile|87.4|100.0|85.6|87.2|69.4|95|
|United Kingdom|87.1|100.0|88.0|85.2|72.6|90|
|Iceland|86.9|100.0|86.3|76.3|71.8|100|
|Belarus|86.7|100.0|85.4|87.5|65.4|95|
|Singapore|86.6|100.0|99.7|64.1|88.9|80|
|Colombia|85.9|100.0|82.9|92.3|74.2|80|
|Cyprus|85.1|100.0|88.8|70.5|71.0|95|

45
|Economy|SPI<br>overall<br>score|Pillar 1:<br>Data Use|Pillar 2: Data<br>Services|Pillar 3: Data<br>Products|Pillar 4: Data<br>Sources|Pillar 5: Data<br>Infrastructure|
|---|---|---|---|---|---|---|
|Romania|84.3|90.0|94.2|76.5|75.9|85|
|Russian Federation|84.1|93.4|87.6|76.5|72.8|90|
|Mongolia|84.0|100.0|97.2|89.7|73.4|60|
|Bulgaria|83.9|90.0|91.3|75.7|72.4|90|
|North Macedonia|83.5|100.0|87.5|74.6|75.3|80|
|Albania|83.4|90.0|69.8|87.2|70.1|100|
|Philippines|83.4|100.0|90.6|89.8|81.4|55|
|West Bank and<br>Gaza|83.4|100.0|92.1|73.1|66.7|85|
|Israel|83.3|100.0|91.1|70.9|59.3|95|
|Croatia|83.1|90.0|87.5|72.3|71.0|95|
|Armenia|82.8|90.0|85.4|86.6|61.9|90|
|Moldova|82.8|90.0|95.4|75.5|68.0|85|
|Thailand|82.5|100.0|81.3|91.5|54.8|85|
|South Africa|82.4|80.0|86.0|87.6|73.4|85|
|Kyrgyz Republic|81.5|100.0|81.0|91.8|54.4|80|
|Serbia|80.8|100.0|74.5|86.1|73.6|70|
|Saudi Arabia|80.8|100.0|88.2|71.6|79.1|65|
|Brazil|80.5|90.0|87.2|80.2|75.3|70|
|Malta|80.3|100.0|86.1|65.6|74.6|75|
|Egypt, Arab Rep.|79.6|100.0|77.1|83.9|67.0|70|
|United Arab<br>Emirates|79.5|100.0|79.6|71.2|67.0|80|
|Ecuador|79.2|100.0|89.1|89.8|56.9|60|
|Sri Lanka|79.1|100.0|81.8|78.0|80.4|55|
|Indonesia|79.0|100.0|91.1|90.2|53.5|60|
|Ukraine|78.9|100.0|53.8|87.1|58.5|95|
|Kazakhstan|78.2|90.0|89.3|89.4|62.3|60|
|Jordan|78.2|80.0|90.4|87.6|62.9|70|
|Montenegro|78.1|100.0|69.9|83.2|57.2|80|
|Uruguay|77.7|100.0|87.9|89.1|56.7|55|

46
|Economy|SPI<br>overall<br>score|Pillar 1:<br>Data Use|Pillar 2: Data<br>Services|Pillar 3: Data<br>Products|Pillar 4: Data<br>Sources|Pillar 5: Data<br>Infrastructure|
|---|---|---|---|---|---|---|
|Mauritius|77.3|90.0|85.5|80.9|60.1|70|
|Malaysia|76.6|80.0|87.6|85.1|75.4|55|
|Paraguay|75.8|90.0|69.4|87.7|57.1|75|
|Tunisia|75.1|90.0|89.5|82.8|58.4|55|
|India|74.2|80.0|87.7|86.3|62.0|55|
|El Salvador|73.8|90.0|78.8|78.3|51.7|70|
|Azerbaijan|73.5|80.0|68.8|82.5|66.1|70|
|Peru|73.3|90.0|87.3|90.9|53.1|45|
|Dominican Republic|72.4|100.0|68.0|77.1|42.0|75|
|Morocco|72.3|80.0|89.6|85.9|60.8|45|
|Senegal|72.2|80.0|82.0|78.5|45.6|75|
|Viet Nam|72.2|100.0|69.3|77.2|74.2|40|
|Guatemala|72.0|80.0|62.0|85.9|62.1|70|
|Myanmar|72.0|100.0|67.4|85.3|42.1|65|
|Argentina|71.8|70.0|78.9|90.2|59.8|60|
|Bolivia|71.2|100.0|66.9|82.0|62.0|45|
|Pakistan|71.1|100.0|61.9|86.8|46.9|60|
|Uganda|70.7|100.0|65.4|81.6|36.8|70|
|Qatar|70.6|100.0|62.1|67.4|58.8|65|
|Bosnia and<br>Herzegovina|70.6|70.0|63.8|77.5|61.8|80|
|Rwanda|70.6|90.0|70.6|79.5|52.8|60|
|Uzbekistan|70.6|80.0|74.7|78.7|44.4|75|
|Panama|70.5|80.0|66.0|87.4|64.1|55|
|Zimbabwe|70.2|100.0|67.0|88.0|36.1|60|
|Bangladesh|69.7|90.0|61.9|85.8|51.0|60|
|Kuwait|69.2|100.0|63.2|66.2|61.5|55|
|Tanzania|67.3|90.0|70.7|76.6|44.4|55|
|Togo|66.7|90.0|63.7|87.0|32.7|60|
|Kenya|66.3|90.0|60.1|76.6|34.9|70|

47
|Economy|SPI<br>overall<br>score|Pillar 1:<br>Data Use|Pillar 2: Data<br>Services|Pillar 3: Data<br>Products|Pillar 4: Data<br>Sources|Pillar 5: Data<br>Infrastructure|
|---|---|---|---|---|---|---|
|Oman|66.1|100.0|46.6|61.2|67.8|55|
|St. Lucia|66.0|70.0|69.6|68.6|66.8|55|
|Seychelles|66.0|90.0|44.2|68.4|57.3|70|
|Cabo Verde|65.7|80.0|64.4|76.1|63.0|45|
|Niger|65.3|90.0|60.8|84.8|30.8|60|
|Liberia|64.9|90.0|65.7|82.3|26.5|60|
|Burkina Faso|64.8|80.0|68.9|81.5|33.8|60|
|Malawi|64.8|90.0|62.0|80.6|46.5|45|
|Barbados|64.6|100.0|57.6|62.2|48.3|55|
|Gambia, The|64.4|80.0|65.5|89.4|32.3|55|
|Brunei Darussalam|64.4|90.0|71.0|57.7|53.2|50|
|Cambodia|64.3|80.0|63.6|81.0|42.0|55|
|Ghana|64.2|66.6|61.8|88.8|44.0|60|
|Fiji|63.2|80.0|63.1|75.4|37.3|60|
|Algeria|63.2|80.0|57.8|82.0|46.0|50|
|Benin|62.6|80.0|69.7|83.6|29.5|50|
|Samoa|62.4|70.0|63.0|78.8|40.5|60|
|Côte d'Ivoire|62.2|80.0|57.7|79.1|29.4|65|
|Zambia|62.1|90.0|60.4|86.7|28.5|45|
|Nepal|62.0|80.0|62.8|85.5|36.6|45|
|Belize|61.9|70.0|64.6|67.2|62.5|45|
|Maldives|61.8|70.0|63.9|82.5|57.7|35|
|Jamaica|61.6|60.0|72.6|77.8|57.9|40|
|Suriname|61.5|50.0|69.2|69.6|58.9|60|
|Botswana|61.2|50.0|68.8|77.8|64.4|45|
|Ethiopia|61.1|90.0|64.5|81.5|29.5|40|
|Honduras|61.0|90.0|62.1|84.1|38.5|30|
|Lao PDR|60.4|76.6|65.5|79.2|40.7|40|
|Tonga|59.9|70.0|63.2|75.9|45.4|45|
|Timor-Leste|59.9|80.0|61.0|64.5|28.8|65|

48
|Economy|SPI<br>overall<br>score|Pillar 1:<br>Data Use|Pillar 2: Data<br>Services|Pillar 3: Data<br>Products|Pillar 4: Data<br>Sources|Pillar 5: Data<br>Infrastructure|
|---|---|---|---|---|---|---|
|China|59.6|83.4|43.8|77.5|43.3|50|
|Bhutan|59.6|80.0|63.9|75.2|38.8|40|
|Bahrain|59.4|80.0|72.8|52.3|61.7|30|
|Sierra Leone|59.2|80.0|65.3|79.0|31.7|40|
|Mali|59.1|80.0|60.7|82.6|27.4|45|
|Mauritania|58.9|80.0|63.2|66.6|24.5|60|
|Iran, Islamic Rep.|58.7|80.0|29.3|70.7|68.6|45|
|Mozambique|58.7|70.0|59.7|76.5|32.2|55|
|Nigeria|58.6|80.0|63.8|77.8|31.5|40|
|Lebanon|58.5|60.0|61.6|79.6|51.3|40|
|Afghanistan|58.0|80.0|59.4|78.6|17.0|55|
|Guinea|57.9|80.0|62.8|76.6|20.2|50|
|Lesotho|57.5|80.0|29.4|76.3|41.7|60|
|Guyana|56.5|70.0|62.7|71.5|33.0|45|
|Iraq|56.3|60.0|64.5|78.3|33.8|45|
|Namibia|55.8|60.0|62.7|77.6|23.6|55|
|Trinidad and<br>Tobago|55.4|60.0|61.2|64.2|36.9|55|
|St. Vincent and the<br>Grenadines|55.3|60.0|67.4|60.9|48.1|40|
|São Tomé and<br>Príncipe|54.8|60.0|60.9|69.2|49.0|35|
|Cameroon|54.5|60.0|64.2|82.1|21.2|45|
|Bahamas, The|54.1|80.0|27.7|49.5|38.5|75|
|Madagascar|53.7|60.0|60.6|78.2|25.0|45|
|Angola|53.5|60.0|60.8|71.3|35.2|40|
|Tajikistan|53.4|80.0|29.2|81.7|46.2|30|
|Nicaragua|52.7|60.0|61.1|64.2|23.3|55|
|Venezuela, RB|52.3|80.0|59.9|62.2|34.1|25|
|Eswatini|51.7|80.0|22.3|71.7|24.3|60|
|Vanuatu|51.2|56.6|59.1|72.2|33.2|35|

49
|Economy|SPI<br>overall<br>score|Pillar 1:<br>Data Use|Pillar 2: Data<br>Services|Pillar 3: Data<br>Products|Pillar 4: Data<br>Sources|Pillar 5: Data<br>Infrastructure|
|---|---|---|---|---|---|---|
|Congo, Dem. Rep.|51.1|70.0|62.4|67.5|15.5|40|
|Burundi|50.7|60.0|62.9|79.7|15.8|35|
|St. Kitts and Nevis|50.0|60.0|66.7|44.8|43.6|35|
|Chad|49.2|63.4|59.2|75.8|17.8|30|
|Somalia|48.4|80.0|47.9|69.7|4.4|40|
|Palau|48.3|40.0|59.6|56.4|45.7|40|
|Solomon Islands|48.2|50.0|59.3|65.8|15.9|50|
|Antigua and<br>Barbuda|48.2|60.0|26.9|64.6|49.3|40|
|Djibouti|46.6|50.0|59.5|63.8|14.5|45|
|Papua New Guinea|46.0|60.0|59.2|70.6|10.1|30|
|Dominica|44.2|60.0|28.3|59.3|43.4|30|
|Kiribati|43.8|40.0|59.5|75.4|18.9|25|
|Sudan|43.6|53.4|57.9|67.8|18.8|20|
|Gabon|42.8|60.0|29.8|66.1|13.2|45|
|Central African<br>Republic|42.6|50.0|58.6|68.8|10.7|25|
|Grenada|41.1|40.0|22.1|68.7|45.0|30|
|Guinea-Bissau|40.0|70.0|23.7|71.7|14.6|20|
|Haiti|39.6|50.0|18.0|71.6|13.3|45|
|Equatorial Guinea|39.0|30.0|59.6|58.7|21.8|25|
|Tuvalu|38.1|40.0|59.4|60.8|15.5|15|
|Congo, Rep.|37.5|50.0|29.4|62.6|20.2|25|
|Marshall Islands|35.5|10.0|58.3|64.0|25.3|20|
|Micronesia, Fed.<br>Sts.|35.3|20.0|59.1|58.6|13.7|25|
|South Sudan|33.8|40.0|37.8|53.9|7.5|30|
|Yemen, Rep.|33.2|46.6|28.0|55.6|16.0|20|
|Nauru|32.6|30.0|37.6|55.4|35.0|5|
|Syrian Arab<br>Republic|31.9|36.6|23.1|55.0|15.0|30|
|Turkmenistan|31.4|60.0|0.5|69.6|11.7|15|

50
|Economy|SPI<br>overall<br>score|Pillar 1:<br>Data Use|Pillar 2: Data<br>Services|Pillar 3: Data<br>Products|Pillar 4: Data<br>Sources|Pillar 5: Data<br>Infrastructure|
|---|---|---|---|---|---|---|
|Libya|24.4|20.0|25.6|53.6|7.6|15|
|American Samoa||40.0||22.6|||
|Andorra||80.0||38.6||15|
|Aruba||60.0||28.5|||
|Bermuda||60.0||27.1|||
|British Virgin<br>Islands||60.0||27.2|||
|Cayman Islands||50.0||28.6|||
|Channel Islands||60.0|||||
|Comoros||50.0||68.2||40|
|Cuba||60.0||69.7|||
|Curacao||80.0||28.5|||
|Eritrea||36.6||51.7||10|
|Faroe Islands||60.0||14.7|||
|French Polynesia||60.0||24.0|||
|Gibraltar||60.0||18.1|||
|Greenland||50.0||21.0|||
|Guam||60.0||22.1|||
|Hong Kong SAR,<br>China||80.0||43.8|||
|Isle of Man||70.0||12.3|||
|Korea, Dem.<br>People's Rep.||30.0||51.6|||
|Kosovo||40.0|66.5||50.3|80|
|Liechtenstein||70.0||38.3|||
|Macao SAR, China||80.0||37.7|||
|Monaco||90.0||41.6|||
|New Caledonia||80.0||31.6|||
|Northern Mariana<br>Islands||60.0||16.0|||
|Puerto Rico||60.0||35.4|||

51
|Economy|SPI<br>overall<br>score|Pillar 1:<br>Data Use|Pillar 2: Data<br>Services|Pillar 3: Data<br>Products|Pillar 4: Data<br>Sources|Pillar 5: Data<br>Infrastructure|
|---|---|---|---|---|---|---|
|San Marino||90.0|60.7|38.1||55|
|Sint Maarten (Dutch<br>part)||50.0||19.7|||
|St. Martin (French<br>part)||40.0||13.6|||
|Turks and Caicos<br>Islands||60.0||31.5|||
|Virgin Islands (U.S.)||60.0||19.0|||

52
# **Table S5. Comparison of SPI to Other Statistical and Development Indices**

|**Index**|**Assessment**<br>**Type**|**Country**<br>**Coverage**|**Time**<br>**Coverage**|**Aggregation**<br>**Methodology**|**Number of**<br>**Indicators**|**Data Sources**|**Methodology Link**|
|---|---|---|---|---|---|---|---|
|**1**<br>SPI|Global|186|2016-2022|Nested Weight<br>Structure|51|Public International<br>Databases (86%),<br>NSO Websites (14%)|https://openknowledge.world<br>bank.org/handle/10986/35301|
|**2**SCI|Global|145|2004-2020|Simple<br>arithmetic|25|Public International<br>Databases (80%),<br>|https://datatopics.worldbank.<br>org/statisticalcapacity/SCIdas<br>|
|||||average||NSO Websites (20%)|hboard.aspx|
|**3**<br>ODIN|Global|192|2015-2022|Nested Weight<br>Structure|44|NSO Websites|https://odin.opendatawatch.<br>com/|
|**4**Open Data<br>Barometer|Global|116|2013-2017|Nested Weight<br>Structure|46|Expert Survey|https://opendatabarometer.org<br>/leadersedition/methodology/|
|**5**<br>Global Data<br>Barometer|Global|109|2021|Nested Weight<br>Structure|55|Expert Survey|https://globaldatabarometer.or<br>g/research/methodology/|
|**6**Ibrahim Index of<br>African Governance<br>Statistical Capacity<br>Measure|Regional|54|2010-2022|Simple<br>arithmetic<br>average|3|Expert Survey (33%),<br>ODIN (33%), SCI<br>databases (33%)|https://mo.ibrahim.foundation<br>/iiag/methodology|
|**7**<br>EU Snapshot tool|Self<br>Assessment|--|--|--|131|Expert Self<br>Assessment|https://ec.europa.eu/eurostat/<br>web/international-statistical-<br>cooperation-tools/capacity-<br>building-tools/the-snapshot|
|**8**UN NQAF self<br>checklist|Self<br>Assessment|--|--|--|87|Expert Self<br>Assessment|https://unstats.un.org/unsd/me<br>thodology/dataquality/tools/|
|**9**<br>Paris21 NSDS self|Self-|--||--|149|Expert Self-|https://www.paris21.org/nsds|
|assessment|Assessment||--|||Assessment|-self-assessment-evaluation-<br>tool|
|**10**World Governance<br>Indicators|Global|214|1996-2022|Unobserved<br>components<br>model|6|Public International<br>Databases|https://papers.ssrn.com/sol3/p<br>apers.cfm?abstract_id=16821<br>30|
|**11**<br>OPHI Global<br>Multidimensional<br>Poverty Index|Global|109|2010-2021|Nested Weight<br>Structure|10|Public International<br>Databases|https://ophi.org.uk/gmpi-<br>2018/|

53
|**12**World Bank|Global|150|Nested Weight|6 Public International|https://www.worldbank.org/e|
|---|---|---|---|---|---|
|Multidimensional||2009-2022|Structure|Databases|n/topic/poverty/brief/multidi<br>|
|Poverty Measure|||||mensional-poverty-measure|
|**13**<br>World Bank Human|Global|174|Weighted|3<br>Public International|https://openknowledge.world|
|Capital Index||2018,<br>2020|multiplication|Databases|bank.org/handle/10986/34432<br>?cid=GGH_e_hcpexternal_en<br>_ext|
|**14**UN Human<br>Development Index|Global|191<br>1990-2021|Geometric<br>Average|3 Public International<br>Databases|https://hdr.undp.org/content/h<br>uman-development-report-<br>2021-22|
|**15**<br>IHME Human<br>Capital Index|Global|195<br>1990-2016|Life<br>expectancy<br>adjusted by<br>educational<br>attainment,<br>learning, and<br>functional<br>health status|5<br>Public International<br>Databases, imputation|https://www.thelancet.com/pd<br>fs/journals/lancet/PIIS0140-<br>6736(18)31941-X.pdf|
|**16**World Bank Women,<br>Business and the Law|Global|190 1971-2022|Nested Weight<br>Structure|35 Expert Survey|https://wbl.worldbank.org/en/<br>methodology|
|**17**<br>European Data Portal<br>Open Data Maturity<br>Assessment|Regional|35<br>2015-2021|Nested Weight<br>Structure|16<br>NSO Questionnaire|https://data.europa.eu/sites/de<br>fault/files/edp_landscaping_in<br>sight_report_n6_2020.pdf|

54
# **Table S6** . **Bivariate Correlation between Statistical Indexes and Key Development Outcomes**

|SDG||GDB||ODB||ODIN||SCI||SPI|
|---|---|---|---|---|---|---|---|---|---|---|
|SDG 1: Extreme Poverty|-0.51***|ODB, ODIN, SCI,<br>SPI|-0.43***|GDB, ODIN, SCI,<br>SPI|-0.48***|GDB, ODB, SCI,<br>SPI|-0.36***|GDB, ODB, ODIN|-0.51***|GDB, ODB,<br>ODIN,|
|SDG 2: Undernourishment|-0.52***|ODB, ODIN, SCI,<br>SPI|-0.52***|GDB, ODIN, SCI,<br>SPI|-0.55***|GDB, ODB, SCI,|-0.57***|GDB, ODB, ODIN,<br>SPI|-0.62***|GDB, ODB SCI|
|SDG 3: Maternal Mortality|-0.45***|ODB, ODIN, SCI,<br>SPI|-0.43***|GDB, ODIN, SCI,<br>SPI|-0.47***|GDB, ODB, SCI,<br>SPI|-0.38***|GDB, ODB, ODIN|-0.51***|GDB, ODB,<br>ODIN|
|SDG 4: Learning Poverty|-0.65***|ODB, ODIN, SCI,<br>SPI|-0.63***|GDB, ODIN, SCI,<br>SPI|-0.7***|GDB, ODB, SCI,<br>SPI|-0.57***|GDB, ODB, ODIN|-0.73***|GDB, ODB,<br>ODIN|
|SDG 5: Women, Business, Law Index|0.55***|ODB, ODIN, SCI,<br>SPI|0.56***|GDB, ODIN, SCI,<br>SPI|0.52***|GDB, ODB, SCI,|0.41***|GDB, ODB, ODIN|0.6***|GDB, ODB|
|SDG 6: Safely Managed Water|0.58***|ODB, ODIN, SCI,<br>SPI|0.56***|GDB, ODIN, SCI,<br>SPI|0.53***|GDB, ODB, SCI,|0.44***|GDB, ODB, ODIN|0.66***|GDB, ODB|
|SDG 7: Access to Electricity|0.48***|ODB, ODIN, SCI,<br>SPI|0.45***|GDB, ODIN, SCI,<br>SPI|0.42***|GDB, ODB, SCI,<br>SPI|0.35***|GDB, ODB, ODIN|0.47***|GDB, ODB,<br>ODIN|
|SDG 8: GDP per capita (2015 constant<br>$)|0.56***|SPI|0.66***||0.32***|SCI,|0.23***|ODIN|0.53***|GDB|
|SDG 9: Manufacturing value added (%<br>of GDP)|0.11**|ODIN|-0.02||0.23***|GDB SCI|0.34***|ODIN, SPI|0.34***|SCI|
|SDG 10: Gini Index|-0.3***|ODB, ODIN SPI|-0.28***|GDB, ODIN, SCI,<br>SPI|-0.36***|GDB, ODB SPI|-0.11**|, ODB|-0.32***|GDB, ODB,<br>ODIN,|
|SDG 11: Population in Slums|-0.46***|ODIN, SCI, SPI|-0.34***|ODIN, SCI,|-0.48***|GDB, ODB, SCI,|-0.54***|GDB, ODB, ODIN,<br>SPI|-0.59***|GDB , SCI|
|SDG 12: Fossil Fuel Subsidies (% of<br>GDP)|-0.23**|ODB, ODIN SPI|-0.23**|GDB, ODIN SPI|-0.13*|GDB, ODB, SCI,<br>SPI|-0.03|ODIN,|-0.16**|GDB, ODB,<br>ODIN,|
|SDG 13: Greenhouse Gas Emissions|0.19**|ODB, ODIN, SCI,<br>SPI|0.14**|GDB, ODIN, SCI,<br>SPI|0.05|GDB, ODB, SCI,<br>SPI|0.16*|GDB, ODB, ODIN,|0.08**|GDB, ODB,<br>ODIN,|
|SDG 14: Marine protected areas|0.47***|ODB|0.55***|GDB|0.17**|SCI|0.18*|ODIN|0.31***||
|SDG 15: Terrestrial Protected Areas|0.18*|ODB, ODIN, SCI,<br>SPI|0.15**|GDB, ODIN, SCI,<br>SPI|0.21***|GDB, ODB, SCI,<br>SPI|0.13**|GDB, ODB, ODIN,|0.22***|GDB, ODB,<br>ODIN,|
|SDG 16: Government Effectiveness|0.69***|ODB, ODIN SPI|0.71***|GDB, ODIN SPI|0.6***|GDB, ODB|0.46***||0.67***|GDB, ODB|
|SDG 17: Total Debt Service|0.07|ODB, ODIN, SCI,|0.03|GDB, ODIN|0.17*|GDB, ODB, SCI,<br>SPI|0.28***|GDB ODIN, SPI|0.25***|ODIN, SCI|
|SDR: SDG Index Overall Score|0.74***|ODB, ODIN, SCI,|0.69***|GDB, ODIN, SCI,|0.72***|GDB, ODB, SCI,|0.65***|GDB, ODB, ODIN|0.82***||

**Note** : The correlations are shown for the listed indices and the averaged value of the SDG over the past two years (to reduce volatility). The indices listed in the right column under each heading do not have a statistically distinguishable correlation coefficient with the index in the heading and the specific SDG in the row. For instance, for SDG1: Extreme Poverty (the first row), the GDB index does not have a statically significantly different correlation from those of the ODB, ODIN, and SPI. We use the <mark>R package “cocor” (Diedenhofen and Much, 2015) to test for the correlations with overlapping samples.</mark>

55
# **Table S7. Bivariate Correlation between Statistical Indexes and Key Development Indices**

|Index||GDB||ODB||ODIN||SCI||SPI|
|---|---|---|---|---|---|---|---|---|---|---|
|Economic Complexity Index|0.66***|ODB, ODIN, SCI, SPI|0.63***|GDB, ODIN, SCI, SPI|0.65***|GDB, ODB, SCI,|0.58***|GDB, ODB, ODIN,|0.72***|GDB, ODB|
|Environmental Performance Index|0.61***|ODB, ODIN|0.59***|GDB, ODIN SPI|0.49***|GDB, ODB SPI|0.07**||0.5***|ODB, ODIN|
|OECD Better Life Index|0.48***|ODB, ODIN, SCI, SPI|0.39**|GDB, ODIN, SCI,|0.58***|GDB, ODB, SCI, SPI|0.15|GDB, ODB, ODIN|0.62***|GDB ODIN|
|UN Human Development Index|0.72***|ODB, ODIN SPI|0.71***|GDB, ODIN SPI|0.65***|GDB, ODB SPI|0.46***||0.69***|GDB, ODB, ODIN|
|WB Human Capital Index|0.74***|ODB, ODIN SPI|0.73***|GDB, ODIN SPI|0.73***|GDB, ODB SPI|0.56***||0.76***|GDB, ODB, ODIN|
|World Press Freedom Index|0.49***|ODB, ODIN SPI|0.54***|GDB , SPI|0.39***|GDB , SPI|0.18**||0.46***|GDB, ODB, ODIN|

**Note** : The correlations are shown for the listed indices and the averaged value of the SDG over the past two years (to reduce volatility). The years used for the indicates are: Economic Complexity Index (2021,2020), Environmental Performance Index (2022), OECD Better Life Index (2018,2017), UN HDI (2021,2020), WB HCI (2020), World Press Freedom Index (2023,2022). The indices listed in the right column under each heading do not have a statistically distinguishable correlation coefficient with the index in the heading and the specific SDG in the row. For instance, for SDG1: Extreme Poverty (the first row), the GDB index does not have a statically significantly different correlation from those of the ODB, ODIN, SPI, and SCI.  We use the <mark>R package “cocor” (Diedenhofen and Much, 2015) to test for the correlations with overlapping samples.</mark>

56
# **Table S8. Relationship between the SDG Index Overall Score from the 2023 Sustainable Development Report and SPI scores, 2016-2022**

||**Model 1**|**Model 2**|**Model 3**|**Model 4**|**Model 5**|**Model 6**|
|---|---|---|---|---|---|---|
|Overall SPI Score|0.540***|0.024*|0.027**||||
||(0.03)|(0.01)|(0.01)||||
|SPI Pillar 1 Score (Data use)||||0.062|0.002|0.002|
|||||(0.04)|(0.00)|(0.00)|
|SPI Pillar 2 Score (Data services)||||0.015|-0.001|0.002|
|||||(0.02)|(0.00)|(0.00)|
|SPI Pillar 3 Score (Data products)||||-0.060**|0.009|0.007|
|||||(0.03)|(0.01)|(0.01)|
|SPI Pillar 4 Score (Data sources)||||0.275***|0.004|0.000|
|||||(0.03)|(0.01)|(0.01)|
|SPI Pillar 5 Score (Data infrastructure)||||<br>0.133***|<br>0.013***|<br>0.014***|
|||||(0.02)|(0.00)|(0.00)|
|Log GDP per capita (constant 2015 US$)|||3.071***|||<br>3.126***|
||||(0.86)|||(0.86)|
|Trade (% of GDP)|||-0.008*|||-0.007|
||||(0.00)|||(0.00)|
|Agriculture, forestry, fishing value added (% of GDP)|||<br>-0.004|||<br>-0.011|
||||(0.04)|||(0.04)|
|Manufacturing value added (% of GDP)|||-0.037|||-0.033|
||||(0.03)|||(0.02)|
|School Enrollment, Primary (% gross)|||<br>0.025**|||<br>0.024**|
||||(0.01)|||(0.01)|
|Year 2017||0.601***|<br>0.546***||0.624***|<br>0.547***|
|||(0.05)|(0.05)||(0.07)|(0.06)|
|Year 2018||<br>0.876***|<br>0.778***||<br>0.955***|<br>0.812***|
|||(0.08)|(0.08)||(0.10)|(0.09)|
|Year 2019||<br>1.267***|<br>1.113***||<br>1.321***|<br>1.120***|
|||(0.09)|(0.09)||(0.10)|(0.10)|
|Year 2020||<br>1.432***|<br>1.393***||<br>1.456***|<br>1.410***|
|||(0.11)|(0.09)||(0.15)|(0.13)|
|Year 2021||<br>1.685***|<br>1.570***||<br>1.608***|<br>1.509***|
|||(0.14)|(0.12)||(0.24)|(0.21)|
|Year 2022||<br>1.797***|<br>1.665***||<br>1.718***|<br>1.587***|
|||(0.15)|(0.15)||(0.25)|(0.23)|
|Constant|31.029***|||42.661***|||
||(2.60)|||(4.03)|||
|Sigmau||5.67|4.77||5.00|4.65|
|_<br>Sigmae||0.73|0.69||0.71|0.66|
|_<br>R2|0.646|0.997|0.997|0.745|0.997|0.997|
|Country FE|No|Yes|Yes|No|Yes|Yes|
|No of countries|146|146|146|146|146|146|
|No of observations<br>**Note**: * p<0.10, ** p<0.05, *** p<0.01. Standard error<br>Development Indicators (WDI) and SPI. In cases where d<br>the nearest available value.|1001<br>s are clustered<br>ata are missin|1001<br>at the count<br>g for a partic|1001<br>ry level. Dat<br>ular covariat|1001<br>a are from t<br>e, the data are|1001<br>he World B<br>imputed fo|1001<br>ank's World<br>rward using|

57
# **Table S9. Relationship between the SDG Index Overall Score from the 2023 Sustainable Development Report and ODIN, Open Data Barometer, and Global Data Barometer scores, 2013-2022**

||ODIN -<br>Model 1|ODIN -<br>Model 2|ODIN -<br>Model 3|ODB -<br>Model 1|ODB -<br>Model 2|ODB -<br>Model 3|GDB -<br>Model 1|
|---|---|---|---|---|---|---|---|
|ODIN Score|0.424***<br>(0.03)|0.005<br>(0.01)|0.008<br>(0.01)|||||
|Open Data Barometer Score||||0.328***<br>(0.03)|0.011**<br>(0.00)|0.011**<br>(0.00)||
|Global Data Barometer Score|||||||0.375***|
||||||||(0.04)|
|Trade (% of GDP)|||-0.009*|||-0.018*||
||||(0.00)|||(0.01)||
|Agriculture, forestry, fishing value added<br>(% of GDP)|||<br>-0.006|||<br>0.012||
||||(0.05)|||(0.06)||
|Manufacturing value added (% of GDP)|||<br>-0.029|||<br>-0.060||
||||(0.03)|||(0.04)||
|School Enrollment, Primary (% gross)|||<br>0.026**|||<br>0.000||
||||(0.01)|||(0.03)||
|Year 2014|||||0.483***|<br>0.409***||
||||||(0.07)|(0.07)||
|Year 2015|||||<br>0.903***|<br>0.758***||
||||||(0.09)|(0.10)||
|Year 2016|||||<br>1.148***|<br>0.907***||
||||||(0.11)|(0.13)||
|Year 2017||0.649***|0.605***||<br>1.577***|<br>1.316***||
|||(0.04)|(0.05)||(0.17)|(0.18)||
|Year 2018||<br>0.968***|<br>0.878***|||||
|||(0.07)|(0.08)|||||
|Year 2019||<br>1.383***|<br>1.239***|||||
|||(009)|(011)|||||
|Year 2020||.<br>1.569***|.<br>1.529***|||||
|||(0.10)|(0.10)|||||
|Year 2021||<br>1.919***|<br>1.817***|||||
|||(0.11)|(0.12)|||||
|Year 2022||<br>2.016***|<br>1.904***|||||
|||(0.13)|(0.16)|||||
|Constant|47.217***|||57.217***|||57.382**<br>*|
||(1.86)|||(1.24)|||<br>(1.63)|
|Sigmau||6.53|5.24||5.89|4.81||
|_<br>Sigmae||0.84|0.77||0.71|0.57||
|_<br>R2|0503|0997|0997|0566|0998|0998|0523|
|<br>Country FE|.<br>No|.<br>Yes|.<br>Yes|.<br>No|.<br>Yes|.<br>Yes|.<br>No|
|<br>No of countries|144|144|144|100|100|100|93|
|No of observations<br>Note: * p<0.10, ** p<0.05, *** p<0.01. Sta<br>Development Indicators (WDI), Open Data|992<br>ndard errors a<br>Watch (ODIN|992<br>re clustered<br>), Global D|992<br>at the cou<br>ata Barome|361<br>ntry level. D<br>ter (GDB),|361<br>ata from the<br>and Open D|361<br>World Ba<br>ata Baromet|93<br>nk's World<br>er (ODB).|

Note: * p<0.10, ** p<0.05, *** p<0.01. Standard errors are clustered at the country level. Data from the World Bank's World Development Indicators (WDI), Open Data Watch (ODIN), Global Data Barometer (GDB), and Open Data Barometer (ODB). In cases where data are missing for a particular covariate, the data are imputed forward using the nearest available value. Estimates with country fixed effects not available for the Global Data Barometer, because the indicator contains only one time period.

58
# **Table S10. Relationship between the Economic Complexity Index and SPI scores, 20162022**

||**Model 1**|**Model**<br>**2**|**Model 3**|**Model 4**|**Model**<br>**5**|**Model 6**|
|---|---|---|---|---|---|---|
|Overall SPI Score|0.047***|0.005|0.004||||
||(0.00)|(0.00)|(0.00)||||
|SPI Pillar 1 Score (Data use)||||0.004|0.001|0.001|
|||||(0.00)|(0.00)|(0.00)|
|SPI Pillar 2 Score (Data<br>services)||||0.003|0.000|0.000|
|||||(0.00)|(0.00)|(0.00)|
|SPI Pillar 3 Score (Data||||-0013***|0005**|0004**|
|products)||||.|.|.|
|||||(0.00)|(0.00)|(0.00)|
|SPI Pillar 4 Score (Data sources)||||0.022***|0.004**|0.004**|
|||||(0.00)|(0.00)|(0.00)|
|SPI Pillar 5 Score (Data||||0014***|-0001|-0001|
|infrastructure)||||.|.|.|
|||||(0.00)|(0.00)|(0.00)|
|Log GDP per capita (constant<br>|||0.169|||0.087|
|2015 US$)|||||||
||||(0.22)|||(0.22)|
|Trade (% of GDP)|||0.001|||0.000|
||||(0.00)|||(0.00)|
|Agriculture forestry fishing|||||||
|, ,<br>|||-0.015|||-0.015|
|value added (% of GDP)|||||||
||||(0.02)|||(0.02)|
|Manufacturing value added (%|||0019***|||0019***|
|of GDP)|||.|||.|
||||(0.01)|||(0.01)|
|School Enrollment, Primary (%<br>|||0.000|||0.000|
|gross)|||||||
||||(0.00)|||(0.00)|
|Year 2017||-0.016|-0.019||-0.015|-0.013|
|||(0.01)|(0.02)||(0.01)|(0.02)|
|Year 2018||-0.024|-0.033||-0.018|-0.018|
|||(0.03)|(0.03)||(0.02)|(0.03)|
|Year 2019||-0.026|-0.036||-0.008|-0.011|
|||(0.02)|(0.03)||(0.02)|(0.03)|
|Year 2020||-0.035|-0.025||-0.048|-0.031|
|||(0.03)|(0.03)||(0.03)|(0.03)|
|Year 2021||-0.064|-0.069||-0.108**|-0.100**|
|||(0.04)|(0.04)||(0.04)|(0.05)|
|Year 2022||-0.065|-0.085||-0.105**|-0.109**|
|||(0.04)|(0.05)||(0.04)|(0.05)|
|Constant|-3.257***|||-1.772***|||
||(0.24)|||(0.35)|||
|Sigmau||0.64|0.47||0.59|0.47|
|_<br>Sigma_e||0.14|0.14||0.14|0.14|
|<br>R2|0493|0982|0982|0598|0982|0983|
|<br>Country FE|.<br>No|.<br>Yes|.<br>Yes|.<br>No|.<br>Yes|.<br>Yes|
|<br>No of countries|<br>121|<br>121|<br>121|<br>121|<br>121|<br>121|
|<br>No of observations<br>**Note**: * p<0.10, ** p<0.05, *** p<<br>the World Bank's World Developm<br>particular covariate,the data are im|826<br>0.01. Standa<br>ent Indicat<br> puted forw|826<br>rd errors<br>ors (WDI)<br>ard using|826<br>are clustered<br>and SPI. In<br>the nearest|826<br>at the count<br>cases where<br>available val|826<br>ry level. Da<br>data are m<br>ue.|826<br>ta are from<br>issing for a|

**Note** : * p<0.10, ** p<0.05, *** p<0.01. Standard errors are clustered at the country level. Data are from the World Bank's World Development Indicators (WDI) and SPI. In cases where data are missing for a <u>particular covariate, the data are imputed forward using the nearest available value.</u>

59
**Table S11. Relationship between the Environmental Performance Index and SPI scores, 2016-2022**

||**Model 1**|**Model 2**|**Model 3**|**Model 4**|**Model 5**|**Model 6**|
|---|---|---|---|---|---|---|
|Overall SPI Score|0.498***|-0.032|-0.038||||
||(0.05)|(0.07)|(0.07)||||
|SPI Pillar 1 Score (Data use)||||0.011|0.040|0.044|
|||||(0.04)|(0.03)|(0.03)|
|SPI Pillar 2 Score (Data services)||||0.073**|0.012|0.010|
|||||(0.03)|(0.02)|(0.02)|
|SPI Pillar 3 Score (Data products)||||-0.542***|-0.003|0.005|
|||||(0.04)|(0.06)|(0.06)|
|SPI Pillar 4 Score (Data sources)||||0.309***|-0.082|-0.093|
|||||(0.04)|(0.06)|(0.06)|
|SPI Pillar 5 Score (Data infrastructure)||||0.227***|-0.036|-0.034|
|||||(0.03)|(0.03)|(0.03)|
|Log GDP per capita (constant 2015 US$)|||3.595|||4.844|
||||(4.43)|||(4.43)|
|Trade (% of GDP)|||0.030|||0.030|
||||(0.02)|||(0.03)|
|Agriculture, forestry, fishing value added (% of<br>GDP)|||0.053|||0.071|
||||(0.15)|||(0.16)|
|Manufacturing value added (% of GDP)|||-0.011|||-0.015|
||||(0.21)|||(0.23)|
|School Enrollment, Primary (% gross)|||-0.019|||-0.017|
||||(0.07)|||(0.06)|
|Year 2017||0.076|-0.060||-0.155|-0.340|
|||(0.17)|(0.21)||(0.28)|(0.32)|
|Year 2018||0.156|-0.083||-0.211|-0.496|
|||(0.35)|(0.42)||(0.47)|(0.54)|
|Year 2019||0.165|-0.137||-0.070|-0.436|
|||(0.37)|(0.49)||(0.46)|(0.59)|
|Year 2020||-8.941***|-8.849***||-9.035***|-8.996***|
|||(0.70)|(0.69)||(0.81)|(0.79)|
|Year 2021||-8.747***|-8.983***||-8.477***|-8.903***|
|||(0.91)|(0.93)||(1.26)|(1.28)|
|Year 2022||-12.403***|-12.978***||-12.188***|-12.988***|
|||(0.98)|(1.10)||(1.26)|(1.37)|
|Constant|19.196***|||54.307***|||
||(3.03)|||(3.11)|||
|Sigmau||8.91|5.23||6.8|5.08|
|_<br>Sigma_e||5.91|5.9||5.28|5.25|
|R2|0.299|0.925|0.926|0.577|0.926|0.927|
|Country FE|No|Yes|Yes|No|Yes|Yes|
|No of countries|158|158|158|158|158|158|
|No of observations<br>**Note**: * p<0.10, ** p<0.05, *** p<0.01. Standard e<br>Development Indicators (WDI) and SPI. In cases wh<br>the nearest available value.|1085<br>rrors are clust<br>ere data are m|1085<br>ered at the co<br>issing for a pa|1085<br>untry level.<br>rticular covar|1085<br>Data are from<br>iate, the data|1085<br>the World<br>are imputed f|1085<br>Bank's World<br>orward using|

60
**Table S12. Relationship between the OECD Better Life Index and SPI scores, 2016-2022**

|**Model 1**|**Model 2**|**Model 3**|**Model 4**|**Model 5**|**Model 6**|
|---|---|---|---|---|---|
|Overall SPI Score<br>0.091***|0.000|0.000||||
|(0.03)|(0.01)|(0.01)||||
|SPI Pillar 1 Score (Data use)|||0.015|-0.004|0.001|
||||(0.03)|(0.00)|(0.00)|
|SPI Pillar 2 Score (Data services)|||0.008|0.002|0.001|
||||(0.01)|(0.00)|(0.00)|
|SPI Pillar 3 Score (Data products)|||-0.040***|0.012*|0.006|
||||(0.01)|(0.01)|(0.01)|
|SPI Pillar 4 Score (Data sources)|||0.039**|0.001|-0.003|
||||(0.02)|(0.01)|(0.01)|
|SPI Pillar 5 Score (Data infrastructure)|||0.051***|-0.008|-0.003|
||||(0.01)|(0.01)|(0.01)|
|Log GDP per capita (constant 2015 US$)||2.265**|||2.120**|
|||(0.86)|||(0.94)|
|Trade (% of GDP)||-0.001|||-0.001|
|||(0.00)|||(0.00)|
|Agriculture, forestry, fishing value added (% of<br>GDP)||0.090*|||0.106*|
|||(0.05)|||(0.05)|
|Manufacturing value added (% of GDP)||-0.050*|||-0.040|
|||(0.03)|||(0.03)|
|School Enrollment, Primary (% gross)||0.030|||0.025|
|||(0.02)|||(0.02)|
|Year 2017|-0.122***|-0.175***||-0.173***|-0.208***|
||(0.03)|(0.04)||(0.05)|(0.05)|
|Year 2018|-0.218**|-0.300***||-0.292***|-0.336***|
||(0.09)|(0.08)||(0.08)|(0.08)|
|Year 2019|-0.218**|-0.340***||-0.256***|-0.354***|
||(0.09)|(0.08)||(0.09)|(0.08)|
|Year 2020|-0.218**|-0.246***||-0.294***|-0.296***|
||(0.10)|(0.08)||(0.09)|(0.08)|
|Year 2021|-0.222*|-0.354***||-0.423***|-0.437***|
||(0.13)|(0.11)||(0.13)|(0.11)|
|Year 2022|-0.222*|-0.413***||-0.424***|-0.492***|
||(0.13)|(0.13)||(0.13)|(0.12)|
|Constant<br>-0.919|||0.126|||
|(2.72)|||(2.80)|||
|Sigmau|1.16|0.6||0.98|0.53|
|_<br>Sigmae|0.23|0.22||0.22|0.22|
|_<br>R2<br>0.127|0.978|0.982|0.432|0.979|0.982|
|Country FE<br>No|Yes|Yes|No|Yes|Yes|
|No of countries<br>43|43|43|43|43|43|
|No of observations<br>287<br>**Note**: * p<0.10, ** p<0.05, *** p<0.01. Standard errors are clus<br>Development Indicators (WDI) and SPI. In cases where data are m<br>the nearest available value.|287<br>tered at the c<br>issing for a p|287<br>ountry level.<br>articular cova|287<br>Data are from<br>riate, the data|287<br>the World<br>are imputed|287<br>Bank's World<br>forward using|

61
# **Table S13. Relationship between the UN Human Development Index and SPI scores, 20162022**

||**Model 1**|**Model 2**|**Model 3**|**Model 4**|**Model 5**|**Model 6**|
|---|---|---|---|---|---|---|
|Overall SPI Score|0.007***|0.000|0.000||||
||(0.00)|(0.00)|(0.00)||||
|SPI Pillar 1 Score (Data use)||||0.000|0.000|0.000|
|||||(0.00)|(0.00)|(0.00)|
|SPI Pillar 2 Score (Data services)||||0.000|0.000|0.000|
|||||(0.00)|(0.00)|(0.00)|
|SPI Pillar 3 Score (Data products)||||-0.003***|0.000|0.000|
|||||(0.00)|(0.00)|(0.00)|
|SPI Pillar 4 Score (Data sources)||||0.005***|0.000|0.000|
|||||(0.00)|(0.00)|(0.00)|
|SPI Pillar 5 Score (Data infrastructure)||||0.002***|0.000|0.000|
|||||(0.00)|(0.00)|(0.00)|
|Log GDP per capita (constant 2015 US$)|||0.038***|||0.039***|
||||(0.01)|||(0.01)|
|Trade (% of GDP)|||0.000|||0.000|
||||(0.00)|||(0.00)|
|Agriculture, forestry, fishing value added (% of<br>GDP)|||0.000|||0.000|
||||(0.00)|||(0.00)|
|Manufacturing value added (% of GDP)|||0.000|||0.000|
||||(0.00)|||(0.00)|
|School Enrollment, Primary (% gross)|||0.000***|||0.000***|
||||(0.00)|||(0.00)|
|Year 2017||0.004***|0.003***||0.004***|0.003***|
|||(0.00)|(0.00)||(0.00)|(0.00)|
|Year 2018||0.008***|0.006***||0.007***|0.006***|
|||(0.00)|(0.00)||(0.00)|(0.00)|
|Year 2019||0.012***|0.010***||0.012***|0.009***|
|||(0.00)|(0.00)||(0.00)|(0.00)|
|Year 2020||0.006***|0.006***||0.005***|0.005***|
|||(0.00)|(0.00)||(0.00)|(0.00)|
|Year 2021||0.005***|0.004***||0.005**|0.004**|
|||(0.00)|(0.00)||(0.00)|(0.00)|
|Year 2022||0.005***|0.003***||0.005**|0.003|
|||(0.00)|(0.00)||(0.00)|(0.00)|
|Constant|0.296***|||0.583***|||
||(0.04)|||(0.04)|||
|Sigmau||0.1|0.04||0.07|0.04|
|_<br>Sigmae||0.01|0.01||0.01|0.01|
|_<br>R2|0.515|0.999|0.999|0.758|0.999|0.999|
|Country FE|No|Yes|Yes|No|Yes|Yes|
|No of countries|160|160|160|160|160|160|
|No of observations<br>**Note**: * p<0.10, ** p<0.05, *** p<0.01. Standard<br>Development Indicators (WDI) and SPI. In cases wh<br>the nearest available value.|1099<br>errors are clus<br>ere data are m|1099<br>tered at the c<br>issing for a p|1099<br>ountry level.<br>articular cova|1099<br>Data are from<br>riate, the data|1099<br>the World<br>are imputed|1099<br>Bank's World<br>forward using|

62
**Table S14. Relationship between the WB Human Capital Index and SPI scores, 2016-2022**

||**Model 1**|**Model 2**|**Model 3**|**Model 4**|**Model 5**|**Model 6**|
|---|---|---|---|---|---|---|
|Overall SPI Score|0.007***|0.001***|0.001***||||
||(0.00)|(0.00)|(0.00)||||
|SPI Pillar 1 Score (Data use)||||0.000|0.000|0.000|
|||||(0.00)|(0.00)|(0.00)|
|SPI Pillar 2 Score (Data services)||||0.000|0.000|0.000|
|||||(0.00)|(0.00)|(0.00)|
|SPI Pillar 3 Score (Data products)||||-0.003***|0.000**|0.000**|
|||||(0.00)|(0.00)|(0.00)|
|SPI Pillar 4 Score (Data sources)||||0.004***|0.000|0.000|
|||||(0.00)|(0.00)|(0.00)|
|SPI Pillar 5 Score (Data infrastructure)||||0.003***|0.000***|0.000***|
|||||(0.00)|(0.00)|(0.00)|
|Log GDP per capita (constant 2015 US$)|||-0.017|||-0.016|
||||(0.01)|||(0.01)|
|Trade (% of GDP)|||0.000|||0.000|
||||(0.00)|||(0.00)|
|Agriculture, forestry, fishing value added (% of<br>GDP)|||-0.001|||-0.001|
||||(0.00)|||(0.00)|
|Manufacturing value added (% of GDP)|||0.000|||-0.001|
||||(0.00)|||(0.00)|
|School Enrollment, Primary (% gross)|||0.000|||0.000|
||||(0.00)|||(0.00)|
|Year 2017||0.019***|0.019***||0.018***|0.018***|
|||(0.00)|(0.00)||(0.00)|(0.00)|
|Year 2018||0.018***|0.019***||0.019***|0.019***|
|||(0.00)|(0.00)||(0.00)|(0.00)|
|Year 2019||0.018***|0.018***||0.018***|0.019***|
|||(0.00)|(0.00)||(0.00)|(0.00)|
|Year 2020||0.012***|0.012***||0.011***|0.011***|
|||(0.00)|(0.00)||(0.00)|(0.00)|
|Year 2021||0.009***|0.010***||0.004|0.004|
|||(0.00)|(0.00)||(0.00)|(0.00)|
|Year 2022||0.009***|0.011***||0.003|0.003|
|||(0.00)|(0.00)||(0.00)|(0.00)|
|Constant|0.127***|||0.411***|||
||(0.03)|||(0.03)|||
|Sigmau||0.09|0.06||0.06|0.05|
|_<br>Sigmae||0.02|0.02||0.02|0.02|
|_<br>R2|0.563|0.991|0.991|0.767|0.992|0.992|
|Country FE|No|Yes|Yes|No|Yes|Yes|
|No of countries|151|151|151|151|151|151|
|No of observations<br>**Note**: * p<0.10, ** p<0.05, *** p<0.01. Standard<br>Development Indicators (WDI) and SPI. In cases wh<br>the nearest available value.|1036<br>errors are clus<br>ere data are m|1036<br>tered at the c<br>issing for a p|1036<br>ountry level.<br>articular cova|1036<br>Data are from<br>riate, the data|1036<br>the World<br>are imputed|1036<br>Bank's World<br>forward using|

63
# **Table S15. Relationship between the World Press Freedom Index and SPI scores, 20162022**

||**Model 1**|**Model 2**|**Model 3**|**Model 4**|**Model 5**|**Model 6**|
|---|---|---|---|---|---|---|
|Overall SPI Score|0.372***|-0.021|-0.007||||
||(0.06)|(0.06)|(0.06)||||
|SPI Pillar 1 Score (Data use)||||-0.049|0.036|0.032|
|||||(0.08)|(0.04)|(0.03)|
|SPI Pillar 2 Score (Data services)||||0.100**|0.003|0.006|
|||||(0.05)|(0.01)|(0.01)|
|SPI Pillar 3 Score (Data products)||||-0.095|-0.020|-0.005|
|||||(0.08)|(0.05)|(0.05)|
|SPI Pillar 4 Score (Data sources)||||0.065|0.000|-0.004|
|||||(0.08)|(0.03)|(0.03)|
|SPI Pillar 5 Score (Data infrastructure)||||0.175***|-0.045**|-0.040*|
|||||(0.05)|(0.02)|(0.02)|
|Log GDP per capita (constant 2015 US$)|||2.530|||2.895|
||||(4.00)|||(3.96)|
|Trade (% of GDP)|||0.046**|||0.043*|
||||(0.02)|||(0.02)|
|Agriculture, forestry, fishing value added (% of<br>GDP)|||-0.065|||-0.051|
||||(0.15)|||(0.15)|
|Manufacturing value added (% of GDP)|||-0.355**|||-0.347**|
||||(0.14)|||(0.13)|
|School Enrollment, Primary (% gross)|||0.049|||0.048|
||||(0.04)|||(0.04)|
|Year 2017||0.048|-0.139||0.107|-0.131|
|||(0.13)|(0.18)||(0.19)|(0.23)|
|Year 2018||0.101|-0.240||0.094|-0.299|
|||(0.27)|(0.34)||(0.32)|(0.39)|
|Year 2019||-0.232|-0.655||-0.126|-0.595|
|||(0.38)|(0.45)||(0.39)|(0.45)|
|Year 2020||-0.097|-0.097||0.139|0.009|
|||(0.51)|(0.49)||(0.63)|(0.61)|
|Year 2021||-0.398|-0.747||0.309|-0.290|
|||(0.75)|(0.76)||(1.10)|(1.12)|
|Year 2022||-6.928***|-7.662***||-6.219***|-7.189***|
|||(0.91)|(1.00)||(1.28)|(1.36)|
|Constant|40.788***|||55.959***|||
||(4.43)|||(7.55)|||
|Sigmau||12.64|11.66||12.3|11.08|
|_<br>Sigma_e||4.02|3.95||3.88|3.83|
|R2|0.153|0.957|0.959|0.192|0.957|0.959|
|Country FE|No|Yes|Yes|No|Yes|Yes|
|No of countries|151|151|151|151|151|151|
|No of observations|1036|1036|1036|1036|1036|1036|
|**Note**: * p<0.10, ** p<0.05, *** p<0.01. Standard e|rrors are clust|ered at the c|ountry level.|Data are from|the World|Bank's World|
|Development Indicators (WDI) and SPI. In cases wh<br>the nearest available value.|ere data are m|issing for a p|articular cova|riate, the data|are imputed|forward using|

64
# **Table S16. Relationship between the Economic Complexity Index and ODIN, Open Data Barometer, and Global Data Barometer scores, 2013-2022**

||ODIN -|ODIN -|ODIN -|ODB -|ODB -|ODB -|GDB -|
|---|---|---|---|---|---|---|---|
||Model 1|Model 2|Model 3|Model 1|Model 2|Model 3|Model 1|
|ODIN Score|0.036***<br>(0.00)|0.002*<br>(0.00)|0.002<br>(0.00)|||||
|Open Data Barometer<br>||||0.028***|0.000|0.000||
|Score||||||||
|||||(0.00)|(0.00)|(0.00)||
|Global Data Barometer|||||||***|
|Score|||||||0.035|
||||||||(0.00)|
|Trade (% of GDP)|||0.000|||-0.001||
||||(0.00)|||(0.00)||
|Agriculture, forestry,||||||||
|<br>fishing value added (% of|||-0.012|||0.008||
|<br>GDP)||||||||
||||(0.02)|||(0.02)||
|Manufacturing value<br>added (% of GDP)|||0.020***|||0.002||
||||(0.00)|||(0.01)||
|School Enrollment,|||0001|||0008**||
|Primar (% ross)|||-.|||.||
|y g|||(0.00)|||(0.00)||
|Year 2014|||||0.006|0.003||
||||||(0.01)|(0.01)||
|Year 2015|||||0.018|0.012||
||||||(002)|(002)||
|Year 2016|||||.<br>-0.017|.<br>-0.026||
||||||(0.02)|(0.03)||
|Year 2017||-0.011|-0.017||-0.021|-0.025||
|||(0.01)|(0.02)||(0.02)|(0.03)||
|Year 2018||-0.019|-0.031|||||
|||(0.02)|(0.03)|||||
|Year 2019||-0.016|-0.030|||||
|||(0.02)|(0.03)|||||
|Year 2020||-0.031|-0.023|||||
|||(0.03)|(0.03)|||||
|Year 2021||-0.038|-0.055|||||
|||(0.03)|(0.04)|||||
|Year 2022||-0.040|-0.071|||||
|||(0.03)|(0.05)|||||
|Constant|-1.753***|||-0.658***|||-1.153***|
||(0.18)|||(0.12)|||(0.17)|
|Sigmau||0.71|0.52||0.7|0.47||
|_<br>Sigmae||0.14|0.14||0.1|0.1||
|_<br>R2|0.378|0.982|0.982|0.411|0.992|0.992|0.405|
|<br>Country FE|<br>No|<br>Yes|<br>Yes|<br>No|<br>Yes|<br>Yes|<br>No|
|<br>No of countries|119|119|119|94|94|94|85|
|No of observations<br>Note: * p<0.10, ** p<0.05<br>Development Indicators (W|833<br>, *** p<0.01.<br>DI), Open D|833<br>Standard erro<br>ata Watch (OD|833<br>rs are clustered<br>IN), Global Da|340<br>at the country<br>ta Barometer (|340<br>level. Data fro<br>GDB), and Op|340<br>m the Worl<br>en Data Bar|85<br>d Bank's World<br>ometer (ODB).|

Note: * p<0.10, ** p<0.05, *** p<0.01. Standard errors are clustered at the country level. Data from the World Bank's World Development Indicators (WDI), Open Data Watch (ODIN), Global Data Barometer (GDB), and Open Data Barometer (ODB). In cases where data are missing for a particular covariate, the data are imputed forward using the nearest available value. Estimates with country fixed effects not available for the Global Data Barometer, because the indicator contains only one time period.

65
# **Table S17. Relationship between the Environmental Performance Index and ODIN, Open Data Barometer, and Global Data Barometer scores, 2013-2022**

||ODIN -|ODIN -|ODIN -|ODB -|ODB -|ODB -|GDB -|
|---|---|---|---|---|---|---|---|
||Model 1|Model 2|Model 3|Model 1|Model 2|Model 3|Model 1|
|ODIN Score|0.467***|-0.027|-0.024|||||
||(0.04)|(0.04)|(0.04)|||||
|Open Data Barometer<br>||||0.464***|0.000|0.000||
|Score||||||||
|||||(0.04)|(0.00)|(0.00)||
|Global Data Barometer|||||||***|
|Score|||||||0.747|
||||||||(0.06)|
|Trade (% of GDP)|||0.028|||0.000||
||||(0.02)|||(0.00)||
|Agriculture, forestry,||||||||
|<br>fishing value added (%<br>of GDP)|||0.008|||0.000||
||||(0.15)|||(0.00)||
|Manufacturing value<br>added (% of GDP)|||-0.297|||0.000||
||||(0.21)|||(0.00)||
|School Enrollment,|||0046|||0000||
|Primary (% gross)|||-.|||.||
||||(0.07)|||(0.00)||
|Year 2014|||||0.000|0.000||
||||||(0.00)|(0.00)||
|Year 2015|||||0.000|0.000||
||||||(000)|(000)||
|Year 2016|||||.<br>0.000|.<br>0.000||
||||||(0.00)|(0.00)||
|Year 2017||0.017|-0.092||0.000|0.000||
|||(0.03)|(0.12)||(0.00)|(0.00)||
|Year 2018||0.142|-0.076|||||
|||(0.21)|(0.30)|||||
|Year 2019||0.142|-0.150|||||
|||(0.21)|(0.37)|||||
|Year 2020||-8.864***|-8.906***|||||
|||(0.66)|(0.66)|||||
|Year 2021||-8.864***|-9.078***|||||
|||(0.66)|(0.68)|||||
|Year 2022||-12.648***<br>(0.75)|-13.080***<br>(0.84)|||||
|Constant|29.690***|||43.746***|||22.852***|
||(1.92)|||(1.67)|||(2.03)|
|Sigmau||8.86|5.49||8.93|6.41||
|_<br>Sigmae||6.41|6.35||0|0||
|_<br>R2|0.275|0.927|0.927|0.535|1.000|1.000|0.579|
|<br>Country FE|<br>No|<br>Yes|<br>Yes|<br>No|<br>Yes|<br>Yes|<br>No|
|<br>No of countries|156|156|156|102|102|102|93|
|No of observations<br>Note: * p<0.10, ** p<0.<br>Development Indicators|1059<br>05, *** p<0.0<br>(WDI), Open|1059<br>1. Standard erro<br>Data Watch (OD|1059<br>rs are clustered<br>IN), Global Da|361<br>at the country le<br>ta Barometer (G|361<br>vel. Data fro<br>DB), and Op|361<br>m the Worl<br>en Data Bar|93<br>d Bank's World<br>ometer (ODB).|

Note: * p<0.10, ** p<0.05, *** p<0.01. Standard errors are clustered at the country level. Data from the World Bank's World Development Indicators (WDI), Open Data Watch (ODIN), Global Data Barometer (GDB), and Open Data Barometer (ODB). In cases where data are missing for a particular covariate, the data are imputed forward using the nearest available value. Estimates with country fixed effects not available for the Global Data Barometer, because the indicator contains only one time period.

66
# **Table S18. Relationship between the OECD Better Life Index and ODIN, Open Data Barometer, and Global Data Barometer scores, 2013-2022**

||ODIN -|ODIN -|ODIN -|ODB -|ODB -|ODB -|GDB -|
|---|---|---|---|---|---|---|---|
||Model 1|Model 2|Model 3|Model 1|Model 2|Model 3|Model 1|
|ODIN Score|0.056***<br>(0.01)|0.000<br>(0.01)|0.001<br>(0.00)|||||
|Open Data Barometer<br>||||0.050***|-0.008**|-0.007*||
|Score||||||||
|||||(0.01)|(0.00)|(0.00)||
|Global Data Barometer||||||||
|Score|||||||0.064***|
||||||||(0.02)|
|Trade (% of GDP)|||-0.001|||-0.002||
||||(0.00)|||(0.01)||
|Agriculture, forestry,||||||||
|<br>fishing value added (%<br>of GDP)|||0.077|||0.017||
||||(0.06)|||(0.05)||
|Manufacturing value<br>added (% of GDP)|||-0.066**|||-0.032||
||||(0.03)|||(0.02)||
|School Enrollment,|||0026|||0009||
|Primary (% gross)|||.|||.||
||||(0.02)|||(0.01)||
|Year 2014|||||0.014|-0.017||
||||||(0.04)|(0.04)||
|Year 2015|||||0.084*|0.041||
||||||(0.05)|(0.05)||
|Year 2016|||||-0.229***|-0.287***||
||||||(0.05)|(0.06)||
|Year 2017||-0.122***|-0.178***||-0.317***|-0.400***||
|||(0.03)|(0.04)||(0.07)|(0.08)||
|Year 2018||-0.217**|-0.313***|||||
|||(0.09)|(0.08)|||||
|Year 2019||-0.217**|-0.359***|||||
|||(0.09)|(0.08)|||||
|Year 2020||-0.217**|-0.262***|||||
|||(0.09)|(0.08)|||||
|Year 2021||-0.217**|-0.368***|||||
|||(0.09)|(0.09)|||||
|Year 2022||-0.218**|-0.420***|||||
|||(0.10)|(0.11)|||||
|Constant|3.174***|||4.396***|||3.295***|
||(0.94)|||(0.49)|||(1.07)|
|Sigmau||1.12|0.48||0.99|0.37||
|_<br>Sigmae||0.23|0.22||0.24|0.24||
|_<br>R2|0.239|0.977|0.981|0.397|0.986|0.986|0.230|
|<br>Country FE|<br>No|<br>Yes|<br>Yes|<br>No|<br>Yes|<br>Yes|<br>No|
|<br>No of countries|41|41|41|38|38|38|30|
|No of observations<br>Note: * p<0.10, ** p<0.<br>Development Indicators|287<br>05, *** p<0.<br>(WDI), Open|287<br>01. Standard er<br>Data Watch (O|287<br>rors are cluster<br>DIN), Global|163<br>ed at the count<br>Data Baromete|163<br>ry level. Data<br>r (GDB), and|163<br>from the Worl<br>Open Data Bar|30<br>d Bank's World<br>ometer (ODB).|

Note: * p<0.10, ** p<0.05, *** p<0.01. Standard errors are clustered at the country level. Data from the World Bank's World Development Indicators (WDI), Open Data Watch (ODIN), Global Data Barometer (GDB), and Open Data Barometer (ODB). In cases where data are missing for a particular covariate, the data are imputed forward using the nearest available value. Estimates with country fixed effects not available for the Global Data Barometer, because the indicator contains only one time period.

67
**Table S19. Relationship between the UN Human Development Index and ODIN, Open Data Barometer, and Global Data Barometer scores, 2013-2022**

||ODIN -|ODIN -|ODIN -|ODB -|ODB -|ODB -|GDB -|
|---|---|---|---|---|---|---|---|
||Model 1|Model 2|Model 3|Model 1|Model 2|Model 3|Model 1|
|ODIN Score|0.006***<br>(0.00)|0.000<br>(0.00)|0.000<br>(0.00)|||||
|Open Data Barometer<br>Score||||0.005***|0.000|0.000||
|||||(0.00)|(0.00)|(0.00)||
|Global Data Barometer|||||||***|
|Score|||||||0.006|
||||||||(0.00)|
|Trade (% of GDP)|||0.000|||0.000*||
||||(0.00)|||(0.00)||
|Agriculture, forestry,||||||||
|<br>fishing value added (%<br>of GDP)|||0.000|||-0.001||
||||(0.00)|||(0.00)||
|Manufacturing value<br>added (% of GDP)|||0.000|||0.001||
||||(0.00)|||(0.00)||
|School Enrollment,|||0000***|||0000||
|Primary (% gross)|||.|||.||
||||(0.00)|||(0.00)||
|Year 2014|||||0.005***|0.004***||
||||||(0.00)|(0.00)||
|Year 2015|||||0.009***|0.007***||
||||||(0.00)|(0.00)||
|Year 2016|||||0.013***|0.010***||
||||||(0.00)|(0.00)||
|Year 2017||0.004***|0.003***||0.016***|0.012***||
|||(0.00)|(0.00)||(0.00)|(0.00)||
|Year 2018||0.007***|0.006***|||||
|||(0.00)|(0.00)|||||
|Year 2019||0.011***<br>|0.009***<br>|||||
|||(0.00)|(0.00)|||||
|Year 2020||0.006***|0.005***|||||
|||(0.00)|(0.00)|||||
|Year 2021||0.005***|0.003***|||||
|||(0.00)|(0.00)|||||
|Year 2022||0.005***<br>(0.00)|0.002**<br>(0.00)|||||
|Constant|0.440***|||0.579***|||0.550***|
||(0.02)|||(0.02)|||(0.02)|
|Sigmau||0.1|0.04||0.09|0.04||
|_<br>Sigmae||0.01|0.01||0.01|0||
|_<br>R2|0.470|0.999|0.999|0.569|0.999|1.000|0.519|
|<br>Country FE|<br>No|<br>Yes|<br>Yes|<br>No|<br>Yes|<br>Yes|<br>No|
|<br>No of countries|160|160|160|101|101|101|95|
|No of observations<br>Note: * p<0.10, ** p<0.<br>Development Indicators|1083<br>05, *** p<0.<br>(WDI), Open|1083<br>01. Standard er<br>Data Watch (|1083<br>rors are cluster<br>ODIN), Global|362<br>ed at the count<br>Data Baromete|362<br>ry level. Data<br>r (GDB), and|362<br>from the Worl<br>Open Data Ba|95<br>d Bank's World<br>rometer (ODB).|

Note: * p<0.10, ** p<0.05, *** p<0.01. Standard errors are clustered at the country level. Data from the World Bank's World Development Indicators (WDI), Open Data Watch (ODIN), Global Data Barometer (GDB), and Open Data Barometer (ODB). In cases where data are missing for a particular covariate, the data are imputed forward using the nearest available value. Estimates with country fixed effects not available for the Global Data Barometer, because the indicator contains only one time period.

68
# **Table S20. Relationship between the WB Human Capital Index and ODIN, Open Data Barometer, and Global Data Barometer scores, 2013-2022**

||ODIN -|ODIN -|ODIN -|ODB -|ODB -|ODB -|GDB -|
|---|---|---|---|---|---|---|---|
||Model 1|Model 2|Model 3|Model 1|Model 2|Model 3|Model 1|
|ODIN Score|0.006***<br>(0.00)|0.000<br>(0.00)|0.000<br>(0.00)|||||
|Open Data Barometer<br>||||0.005***|0.000|0.000||
|Score||||||||
|||||(0.00)|(0.00)|(0.00)||
|Global Data Barometer|||||||***|
|Score|||||||0.006|
||||||||(0.00)|
|Trade (% of GDP)|||0.000|||0.000||
||||(0.00)|||(0.00)||
|Agriculture, forestry,||||||||
|<br>fishing value added (%<br>of GDP)|||-0.001|||-0.001||
||||(0.00)|||(0.00)||
|Manufacturing value<br>added (% of GDP)|||0.000|||0.001||
||||(0.00)|||(0.00)||
|School Enrollment,|||0000|||0001||
|Primary (% gross)|||.|||.||
||||(0.00)|||(0.00)||
|Year 2014|||||0.001|0.001**||
||||||(0.00)|(0.00)||
|Year 2015|||||0.000|0.002**||
||||||(000)|(000)||
|Year 2016|||||.<br>0.000|.<br>0.002*||
||||||(0.00)|(0.00)||
|Year 2017||0.021***|0.021***||0.023***|0.026***||
|||(0.00)|(0.00)||(0.01)|(0.01)||
|Year 2018||0.021***|0.022***|||||
|||(0.00)|(0.00)|||||
|Year 2019||0.022***|0.022***|||||
|||(0.00)|(0.00)|||||
|Year 2020||0.017***|0.017***|||||
|||(0.00)|(0.00)|||||
|Year 2021||0.017***|0.018***|||||
|||(0.00)|(0.00)|||||
|Year 2022||0.017***|0.018***|||||
|||(0.00)|(0.00)|||||
|Constant|0.271***|||0.408***|||0.389***|
||(0.02)|||(0.02)|||(0.02)|
|Sigmau||0.09|0.06||0.09|0.06||
|_<br>Sigmae||0.02|0.02||0.01|0.01||
|_<br>R2|0.532|0.991|0.991|0.595|0.998|0.998|0.531|
|<br>Country FE|<br>No|<br>Yes|<br>Yes|<br>No|<br>Yes|<br>Yes|<br>No|
|<br>No of countries|149|149|149|100|100|100|92|
|No of observations<br>Note: * p<0.10, ** p<0.<br>Development Indicators|1020<br>05, *** p<0.<br>(WDI), Open|1020<br>01. Standard er<br>Data Watch (|1020<br>rors are cluster<br>ODIN), Global|361<br>ed at the count<br>Data Baromete|361<br>ry level. Data<br>r (GDB), and|361<br>from the Worl<br>Open Data Ba|92<br>d Bank's World<br>rometer (ODB).|

Note: * p<0.10, ** p<0.05, *** p<0.01. Standard errors are clustered at the country level. Data from the World Bank's World Development Indicators (WDI), Open Data Watch (ODIN), Global Data Barometer (GDB), and Open Data Barometer (ODB). In cases where data are missing for a particular covariate, the data are imputed forward using the nearest available value. Estimates with country fixed effects not available for the Global Data Barometer, because the indicator contains only one time period.

69
# **Table S21. Relationship between the World Press Freedom Index and ODIN, Open Data Barometer, and Global Data Barometer scores, 2013-2022**

||ODIN -|ODIN -|ODIN -|ODB -|ODB -|ODB -|GDB -|
|---|---|---|---|---|---|---|---|
||Model 1|Model 2|Model 3|Model 1|Model 2|Model 3|Model 1|
|ODIN Score|0.308***<br>(0.06)|-0.030<br>(0.03)|-0.027<br>(0.03)|||||
|Open Data Barometer<br>||||0.336***|0.000|0.000||
|Score||||||||
|||||(0.05)|(0.00)|(0.00)||
|Global Data Barometer|||||||***|
|Score|||||||0.393|
||||||||(0.07)|
|Trade (% of GDP)|||0.041*|||0.000||
||||(0.02)|||(0.00)||
|Agriculture, forestry,||||||||
|<br>fishing value added (%<br>of GDP)|||-0.108|||0.000||
||||(0.15)|||(0.00)||
|Manufacturing value<br>added (% of GDP)|||-0.185|||0.000||
||||(0.13)|||(0.00)||
|School Enrollment,|||0060|||0000||
|Primary (% gross)|||.|||.||
||||(0.04)|||(0.00)||
|Year 2014|||||0.000|0.000||
||||||(0.00)|(0.00)||
|Year 2015|||||0.000|0.000||
||||||(000)|(000)||
|Year 2016|||||.<br>0.000|.<br>0.000||
||||||(0.00)|(0.00)||
|Year 2017||-0.004|-0.225**||0.000|0.000||
|||(0.05)|(0.11)||(0.00)|(0.00)||
|Year 2018||0.139|-0.290|||||
|||(0.20)|(0.28)|||||
|Year 2019||-0.151|-0.669*|||||
|||(0.27)|(0.35)|||||
|Year 2020||0.015|0.095|||||
|||(0.44)|(0.42)|||||
|Year 2021||-0.314|-0.739|||||
|||(0.45)|(0.49)|||||
|Year 2022||-7.029***|-7.898***|||||
|||(0.77)|(0.86)|||||
|Constant|50.789***|||57.233***|||53.384***|
||(3.01)|||(2.51)|||(2.81)|
|Sigmau||12.98|12.12||12.78|12.03||
|_<br>Sigmae||4.19|4.08||0|0||
|_<br>R2|0.121|0.957|0.959|0.242|1.000|1.000|0.186|
|<br>Country FE|<br>No|<br>Yes|<br>Yes|<br>No|<br>Yes|<br>Yes|<br>No|
|<br>No of countries|150|150|150|104|104|104|94|
|No of observations<br>Note: * p<0.10, ** p<0.<br>Development Indicators|1031<br>05, *** p<0.0<br>(WDI), Open|1031<br>1. Standard er<br>Data Watch (O|1031<br>rors are clustere<br>DIN), Global|362<br>d at the country<br>Data Barometer|362<br>level. Data fr<br>(GDB), and O|362<br>om the Wor<br>pen Data Ba|94<br>ld Bank's World<br>rometer (ODB).|

Note: * p<0.10, ** p<0.05, *** p<0.01. Standard errors are clustered at the country level. Data from the World Bank's World Development Indicators (WDI), Open Data Watch (ODIN), Global Data Barometer (GDB), and Open Data Barometer (ODB). In cases where data are missing for a particular covariate, the data are imputed forward using the nearest available value. Estimates with country fixed effects not available for the Global Data Barometer, because the indicator contains only one time period.

70