The Statistics Newsletter From the OECD s tatis tics and data communit y to the ex tended OECD s tatis tic al net work
FEATURING + Going granular: The new OECD Municipal Migration Database + Official statistics at the fingertips of Power BI users around the globe + New platform for smarter financing for development data
THE LATEST OECD COVID-19 RECOVERY DASHBOARD IPAC DASHBOARD oe.cd/statisticsnewsletter Issue No. 75, December 2021
Contents 3
Going Granular: The new OECD Municipal Migration Database
Lukas Kleine-Rueschkamp (lukas.kleine-rueschkamp@oecd.org), Cem Özgüzel (cem.ozguzel@oecd.org), Centre for Entrepreneurship, SMEs, Regions and Cities, OECD
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Official statistics at the fingertips of Power BI users around the globe
Caroline Bernreiter (caroline.bernreiter@oecd.org), Jonathan Challener (jonathan.challener@oecd.org), Statistics and Data Directorate, OECD
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New platform for smarter financing for development data
Sasha Ramirez-Hughes (sasha.ramirez-hughes@oecd.org), Paris21, Statistics and Data Directorate, OECD
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Recent publications
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Forthcoming meetings
The Statistics Newsletter is published by the OECD Statistics and Data Directorate. This issue and previous issues can be downloaded from http://oe.cd/statisticsnewsletter To receive the OECD Statistics Newsletter by email, you can sign up at https://oe.cd/statsnews-signup Follow us on
@OECD_STAT
Editor-in-Chief: Paul Schreyer Editors: Ashley Ward, Annabelle Mourougane and Jorrit Zwijnenburg Technical support: Sonia Primot Contact us at SDD.CommTeam@oecd.org
2 The OECD Statistics Newsletter - Issue No. 75, December 2021
Going Granular The new OECD Municipal Migration Database Lukas Kleine-Rueschkamp (lukas.kleine-rueschkamp@oecd.org), Cem Özgüzel (cem.ozguzel@oecd.org), Centre for Entrepreneurship, SMEs, Regions and Cities, OECD
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igration has risen to the top of the policy Constructing the OECD Municipal Migration agenda in recent years, and not just at Database (MMD) the national or international level. Many regions in OECD countries face major As part of the OECD project The Contribution of Migration demographic change and a declining to Regional Development (OECD, forthcoming), the supply of labour as their population age. To alleviate OECD has engaged in an extensive data collection these challenges and reap the benefits of migration effort. The resulting novel dataset (Astruc-Le Souder for local and regional development, the integration of et al., forthcoming) offers unprecedentedly detailed migrants is crucial, making it one of the most pressing information on the subnational geography of migration policy challenges in OECD countries. While the patterns in OECD countries. Based on data from continuous of migration and the size of migrant communities differ population registers as well as censuses, the database from country to country, subnational contains population statistics for 22 differences within countries also tend A new OECD database OECD member countries between to be significant. offers unprecedentedly 2000 and 2020, primarily at the level of municipalities with a few exceptions detailed geographic To support effective migration such as Germany (districts / Kreise) information on policy design, policymakers need and the United States and Canada migration to understand the different types of (Census tracts /Census subdivisions).3 challenges and difficulties migrants face. Foremost, this The main characteristics available at the municipal level requires comprehensive and detailed data on migrants, include country of origin, age and sex. The data have in particular their geographic distribution across different been collected using Application Programming Interfaces regions and cities within OECD countries. As documented (APIs) when possible (for 19 countries) or directly from by previous research, the foreign-born population the national statistical institutes. Data harmonisation (i.e., migrant population) differs from the native-born ensures consistency of the data over time despite population in terms of where they choose to live, with changes to municipal boundaries. This project will be migrants being more geographically concentrated in officially launched in January 2022. Soon after, the specific areas. OECD Municipal Migration Database (MMD) will be made public and will then be updated and refined on Existing subnational datasets on migrants and previous an ongoing basis. OECD analysis are limited to large administrative regions such as states in the US or federal states in The Municipal Migration Database (MMD) offers new Germany, called TL2 regions.1,2 Such data may provide opportunities to examine the spatial settlement patterns only crude information on migration as it could miss of migrants at a highly granular level, going beyond important differences within those regions, especially large regions. While the level of granularity depends on in very large and populous regions. For example, some the number of municipalities or census tracts in each regions in OECD countries such as Germany and the country, the new dataset provides additional insightful US have populations of 10 million inhabitants or more, spatial information, as it can be aggregated to larger and extend for thousands of square kilometres. As a geographic levels. For example, the new dataset enables result, data for such large territories and populations consistent international comparisons of migration trends often obscure potentially interesting and meaningful across small regions (i.e. TL3), metropolitan areas or by intra-regional discrepancies. Another drawback of such Degree of Urbanisation. As a result, it is now possible regional data is that it lacks the granularity to examine to measure the concentration of migrants within regions important migration trends in rural, urban or metropolitan and cities of all sizes, and it can also be used to analyse areas across the OECD. possible intra-metropolitan patterns of segregation.
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Moreover, the consistency of the dataset (with respect to municipal boundaries) facilitates tracking changes in settlement patterns of migrants over time. Migrants concentrate more in metropolitan areas and cities than the native-born Within OECD countries, significant spatial differences in migration exist. Figure 1 displays the share of migrants among the local population across OECD countries with granular population data on foreign-born individuals. The MMD reveals clear geographic differences, especially in countries with detailed geographic breakdown (i.e. information on small administrative units), such as France, Spain and Italy. In France, migrants are particularly concentrated in and around large cities. In Spain, the data show the large concentration in municipalities that surround the major cities such as Madrid, Barcelona and Valencia, as well as in the communities along the Mediterranean coast. In OECD countries, the migrant population share has increased in recent years, reaching 12% in 2019. Using the novel granular data on migration contained in the MMD,
OECD analysis shows that migrants are significantly more concentrated in specific types of regions than the native-born population. More than half of the foreignborn population (53%) live in large metropolitan regions (small regions that include a metropolitan area of 1.5 million inhabitants), compared to only 40% of natives (Figure 1).4 Less than a fifth of migrants (19%) reside in non-metropolitan regions, compared to almost 30% of the native-born population. The difference in the location of migrants and natives is particularly striking in regions near a metropolitan area and remote regions, where only 6% and 3% of migrants live, respectively. Among the native-born population, those regions account instead for 12% (regions near a metropolitan area) and 5% (remote regions) of the entire population Differences in urbanisation offer another insightful perspective on the geography of migrants and the changes over time. Using the Degree of Urbanisation methodology5 to distinguish different types of settlement for European countries and the novel granular migration dataset shows that cities – defined as local units above 50 000 inhabitants with a population density of over 1 500 inhabitants per square kilometre – have significantly
Figure 1. Share of foreign-born population in municipalities and census tracts in the OECD, 2020 Population share of foreign-born across municipalities and census tracts, 2020 or latest available year
Note: The maps show the population share of foreign-born individuals across municipalities or other granular administrative units in OECD countries. Data are for 2020 or the latest available year. The underlying sample covers the entire local resident population. Source: Based on data from Astruc - Le Souder et al. (forthcoming).
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Figure 2. Distribution of the foreign- and native-born population by type of TL3 region, 2020 Distribution of foreign- and native-born population by type of OECD (TL3) region, 2020 or latest available year
Foreign-born
Share (%) 60%
Native-born
53%
50% 40%
40%
28%
30%
31%
20% 10% 0%
6% Large Metropolitan Region
Metropolitan region
12%
10%
12% 5%
3%
Region near a metropolitan area
Regions with/near a small-medium city
Remote region
Note: Footnote 5 describes and explains the classification of small (TL3) regions by their access to metropolitan areas. The underlying sample covers the entire local resident population. Data are for 2020 or the latest available year. Source: Author’s elaboration based on data from Astruc - Le Souder et al. (forthcoming).
higher migrant population shares compared with other areas in almost all OECD countries with available data.6 For example, in Austria, Belgium, Australia and France, migrants made up at least twice as share of the population in cities than in towns and semi-dense areas or rural areas in 2019. The spatial differences are
particularly striking in Belgium and the Netherlands, where migrants account for 33% (Belgium) and 17% (Netherlands) of the population in cities but only 12% (Belgium) and 7% (Netherlands) in towns and semidense areas, with rural areas reporting even lower migrant population shares. However, in various other
Figure 3. Share of migrants across OECD countries by Degree of Urbanisation, 2020 Foreign-born population share by Degree of Urbanisation, 2020 or latest available year
Share (%)
80% 75% 70% 65% 60% 55% 50% 45% 40% 35% 30% 25% 20% 15% 10% 5% 0%
Cities
Towns and semi-dense areas
Rural areas 77%
Note: Note: 2020 or latest available year Data for the United Kingdom are limited to England and Wales. The underlying sample covers the entire local resident population. Source: Author’s elaboration based on data from Astruc - Le Souder et al. (forthcoming).
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OECD countries, the migrant community is more equally spread out along the urban-rural continuum. In Italy, differences between cities (12%), towns and semi-dense areas (11.1%) and rural areas (9.5%) are relatively small. Additionally, cities and towns and semi-dense areas have relatively similar migrant population shares in both Ireland and Spain. More effective subnational migration policies with more granular data To manage the integration of migrants successfully, policy makers in OECD countries require more detailed and informative data on migrants and migration flows. The need for detailed, targeted, data is particularly important for regional development policies because migration and its potential economic or demographic benefits differ widely within countries, which could weaken the effectiveness of policies implemented at the national level policies. From a policy perspective, understanding the spatial distribution of migrants is the first step to adopting tailored and targeted policies to fit local conditions and challenges. The new OECD dataset presented in this article offers a novel source of subnational data on migration. It not only entails unprecedentedly detailed geographic information for 22 OECD countries but also supports policy design by enabling analysis of how migration differs across cities, metropolitan and rural areas.
Notes 1 Regions within the 37 OECD countries are classified on two territorial levels reflecting the administrative organisation of countries. The 398 OECD “Territorial Level 2” (TL2) regions are those at the highest subnational administrative level, for example, the federal states in Germany. For more, see: OECD (2020), OECD Territorial Grids, http://stats.oecd. org/wbos/fileview2.aspx?IDFile=cebce94d-9474-4ffc-b72a-d731fbdb75b9. 2 See, for example, Diaz Ramirez, M., et al. (2018), "The integration of migrants in OECD regions: A first assessment", OECD Regional Development Working Papers, No. 2018/01, OECD Publishing, Paris, https://doi.org/10.1787/fb089d9a-en. 3 The dataset covers the following countries: Australia, Austria, Belgium, Canada, Denmark, Finland, France, Germany, Ireland, Italy, Japan, Korea, Luxembourg, Mexico, Netherlands, Norway, Portugal, Spain, Sweden, Switzerland, the United Kingdom (England and Wales) and the United States of America. 4 To assess differences in socio-economic trends in regions, OECD groups small regions (TL3) based on the presence/absence of metropolitan areas and the extent to which the latter is accessible by the population living in each region. According to this typology, TL3 regions are classified as metropolitan if more than half of their population lives in a Functional Urban Area (FUA) of at least 250 000 inhabitants and as non-metropolitan otherwise. A metropolitan region becomes a large metropolitan region if the FUA accounting for more than half of the regional population has over 1.5 million inhabitants. In turn, the typology further classifies non-metropolitan regions based on the size of the FUA that is most accessible to the regional population. 5 The Degree of Urbanisation is a methodology to classify cities, towns & semi-dense areas, and rural areas for international comparative purposes. The method proposes three types of areas reflecting the urban-rural continuum instead of the traditional urban–rural dichotomy. The methodology was developed jointly by the OECD and other international organizations, and has been endorsed at the UN Statistical Commission as the recommended method to make international statistical comparisons between cities, urban and
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rural areas. Two recent global application of the definition are made in the OECD-EU publication Cities in World. A new perspective on urbanisation (OECD-EU, 20204) and Applying the Degree of Urbanisation: A Methodological Manual to Define Cities, Towns and Rural Areas for International Comparisons (OECD et al., 20216) 6 Figure 2 combines data provided by Eurostat for European countries with the new granular migration dataset for non-European countries. For the latter, local areas such as municipalities can be categorised by the degree of urbanisation using grid level information on population size and density of those areas (European Commission, and Statistical Office of the European Union, 20215).
References Astruc-Le Souder, M. et al. (forthcoming), “Going granular: a municipal migration database”, OECD Regional Development Working Papers. European Commission, and Statistical Office of the European Union (2021), Applying the Degree of Urbanisation — A methodological manual to define cities, towns and rural areas for international comparisons, http://dx.doi.org/10.2785/706535. European Union et al. (2021), Applying the Degree of Urbanisation, http://dx.doi. org/10.2785/706535. OECD (forthcoming), Contribution of Migration to Regional Development, OECD Publishing, Paris. OECD et al. (2021), Applying the Degree of Urbanisation: A Methodological Manual to Define Cities, Towns and Rural Areas for International Comparisons, https://doi. org/10.1787/4bc1c502-en. OECD-EU (2020), Cities in the World: A New Perspective on Urbanisation, OECD Publishing, Paris, https://doi.org/10.1787/d0efcbda-en.
Measuring the value-added of accommodation-sharing services Digitalisation has changed traditional production mechanisms, affecting the way in which transactions take place and leading to the introduction of new products, amongst other changes. The measurement of such new products and transactions, necessary to paint a clear picture of how digitalisation is affecting our economies, is a pressing challenge for statisticians. The OECD has developed the Digital Supply-and-Use Tables (SUTs) framework, which can provide more insight on the digital economy, including new types of transactions and new digitally-enabled products. Some of the most prominent players in the digital economy are digital intermediary platforms that, in exchange for a fee, facilitate peer-to-peer transactions, such as Airbnb. Since its launch in 2008, Airbnb has grown to become a trusted community marketplace, offering accommodation in over 191 countries via over 4 million hosts. During the first half of 2021, the OECD undertook a project to derive estimates for the value-added of digital accommodation-sharing services using publicly available data, including data from the OECD Supply-Use Table (SUT) database. The model uses a mixture of data derived from national accounts and data from private sources to estimate the level of output and value-added created by Airbnb hosts. By using available variables such as market share, the number of nights rented out in a year, revenues to the hosts, intermediate costs of renting out a room, and fees charged to guests and owners, it is possible to estimate the production value and the intermediate consumption involved when a room or an apartment is rented via Airbnb. The difference between the two gives the value-added of Airbnb hosts. The results of this project show that the value-added from Airbnb hosts is nontrivial, potentially accounting for around 2-4% of the accommodation and food service industry in the countries investigated. Value-added from Airbnb hosts, proportion of Accommodation and food services value-added, 2018 12.0% 10.0% 8.0% 6.0% 4.0% 2.0% 0.0%
Australia
Canada
Denmark
France
Netherlands
Norway
United Kingdom
United States
This project fits into the Digital SUTs framework by providing insight into a specific element of the digital economy and its impact on national accounts. Similar work streams are underway for other components of the Digital SUTs, with various countries contributing to the work. The aim is to have Digital SUTs for a large number of countries within the coming years. For a recent example, please see work done by Statistics Canada. Download more information about the Digital SUTs framework (PDF) For further information, please contact the OECD Statistics and Data Directorate. Written by Ina Tobiassen, OECD Statistics and Data Directorate
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OECD COVID-19 Recovery Dashboard The OECD COVID-19 Recovery Dashboard is a tool for policy makers and citizens to monitor efforts to revive economic activity and fulfil the shared commitment of OECD Member countries to build back better. It features twenty indicators that assess the speed and quality of the recovery, taking into account the strength of economic performance as well as the way in which countries address inequalities, accelerate the green transition and build resilience in the face of future challenges. In line with the OECD’s multi-dimensional approach to measuring progress, the indicators are not aggregated or ranked according to their importance. Instead, they are presented alongside one another to provide a comprehensive picture of how a country is doing in the context of the recovery. The choice of indicators was made in consultation with OECD Members and followed a number of key considerations, including international comparability, country coverage, ease of visualisation and complementarity with existing OECD measurement frameworks, among others. A concerted effort was made to ensure that the Dashboard is aligned with the Sustainable Development Goals (SDGs), reflecting the importance not only of building back, but building towards established medium and long-term agendas; for example, by mainstreaming gender across the Dashboard. The rapidly changing context of the pandemic underscored the importance of timely statistics to inform policy decisions. When designing economic and sanitary measures, policy makers were in need of real-time data on various domains that may affect people’s lives, including business dynamics, labour market developments, living conditions and the well-being impact of the pandemic. In response to such demands, National Statistics Offices (NSOs) launched initiatives to increase the frequency and quality of data collection, in an effort to provide new data products. In some cases, these efforts resulted in quarterly or weekly household “pulse” surveys that incorporated measurements on mental health, life satisfaction, time use and discrimination. NSOs also worked to further leverage public sector data – by using VAT data to monitor business activity, for example – and deepened their collaboration with private sector companies – by monitoring consumption through anonymised data of financial transactions, for instance. The Recovery Dashboard recognises the importance of timely data, for example by incorporating near-real time data on economic activity from the OECD Weekly Tracker, an OECD tool that applies a machine learning algorithm to Google Trends data to estimate weekly GDP developments. The dashboard also features data from the Gallup World Poll, a private survey provider, that delivers timely insights on important aspects of well-being. In the near future, the Dashboard will incorporate nowcasted estimates on income inequality and GHG emissions to bridge the lag time of these statistics. In response to the lessons learnt from this crisis, the OECD will continue to work with NSOs to strive for more timely official statistics in areas that are important for the economy, for people and for the environment. The OECD COVID-19 Recovery Dashboard has been developed by the OECD Centre on Well-being, Inclusion, Sustainability and Equal Opportunity (WISE) as part of an OECD-wide collaboration. For more information, contact: wellbeing@oecd.org.
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Official statistics at the fingertips of Power BI users around the globe Caroline Bernreiter (caroline.bernreiter@oecd.org), Jonathan Challener (jonathan.challener@oecd.org), Statistics and Data Directorate, OECD
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et us start with two questions statisticians, data producers and analysts might face: With hundreds, thousands, or even millions of rows of data stored in my data warehouse, how do I effectively tell a story using that data? How can I help my users identify and understand patterns in this massive amount of information to support crucial evidence-based decisions? The total amount of data created, captured, copied, and consumed globally is estimated at around 64.2 zettabytes in 2020, almost doubling every two years. As the data deluge continues to grow, with more data sources, new technologies, and changing user habits, statistical organisations have recognised the need to adapt from primarily publishers of official statistics to storytellers. Taking on this new role and the associated challenges brings with it an increasing need to visualise their data in new ways. This brings us back to the previous questions and poses a new one: which technical solution delivers the best understanding of these massive and growing datasets, allowing us to identify patterns and draw out key stories? Power BI and SDMX: Empowering users to discover insights in numbers
However, with these available tools, a challenge remains: How do you find and access the multiplicity of sources of official statistics from around the globe? Here is where Statistical Data and Metadata eXchange (SDMX) comes into play. An international open standard, it is used as lingua franca by more and more statistical organisations to effectively produce and disseminate official statistics. They are made available through a standard Application Programming Interface (API), a set of rules that define how computers or applications communicate with one another. The OECD-led Statistical Information System Collaboration Community (SIS-CC), a leading open source community in the field of official statistics is using SDMX as its standard interface for data sourcing and data visualisation and has now enhanced its functionalities by connecting to Power BI. Combining Power BI and SDMX brings a vast audience closer to official statistics The SIS-CC SDMX Power BI connector brings these technologies together, enabling the easy creation of Power BI reports from SDMX data sources. This Power BI connector has been developed open source, freely available by the SIS-CC. The connector has now been
Data visualisation is a powerful tool to find the story in the data and communicate that story to others – a picture might be worth a thousand words, but a good Dataviz is worth more! Microsoft Power BI is one tool statisticians are using to create and publish rich visualisations.
What's Power BI? Power BI is a business analytics service offering data preparation capabilities, visual-based data discovery, interactive dashboards, and augmented analytics. It can be run as Software as a Service (SaaS) over the cloud, or on premises in Power BI report server or as a Power BI desktop, including a free version, for power users authoring dashboards requiring on premise and private data sources.
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Figure 1: According to the research and consulting company Gartner, Microsoft Power BI is a leader in the data visualisation space. This graphic was originally published by Gartner as part of a larger research document in February 2021 and has been augmented by the authors to highlight Microsoft. For more information, please consult the Gartner website: https://www.gartner.com/en/documents/3996944.
officially certified by Microsoft, allowing users of official statistics worldwide to easily connect, shape, visualise, and share data insights through Power BI. As noted by Eric Anvar, Head of Smart Data at the OECD: “The SIS-CC SDMX Power BI connector is another smart way of interconnecting ecosystems, that is the Power BI user base and producers of official statistics, as a way to produce value out of data. We foresee that the semantic power of SDMX will continue to contribute greatly to accessibility of official statistics, in ways similar to the SIS-CC SDMX Power BI connector”. A Use Case of SDMX in Power BI: Tracking Trade Flows in Pacific Island Countries The Pacific Community, one of the 15 members of the SIS-CC, is increasingly using Power BI to visualise content from the Pacific Data Hub (PDH.Stat) database. PHD.Stat, the flagship component of the Pacific Data Hub (SDMX-powered .Stat Suite software), is used for publishing regional statistics as structured datasets, providing major opportunities for optimising the dissemination and accessibility of Pacific statistics through a wide range of channels including MS Power BI. A good example of this is the recently created International Merchandise Trade Statistics dashboard.
To create a dashboard such as this one, a user accesses the desired PDH.Stat dataset via the Power BI-SDMX Connector, transforms and formats the data, and then hooks it up to any number of the wide array of visualisations which Power BI has on offer. This whole process is significantly streamlined with the Power BI-SDMX Connector reducing the import and data transformation process to less than 10% of the steps required when using a custom script. Novice users can now more easily add, transform and format data without full knowledge of the underlying structure of the data. “It’s amazing to see how quickly a valuable decisionsupport dashboard like this could be put together through Power BI” said Phil Bright, GIS, Innovation and Dissemination Lead from the Pacific Community Statistics for Development Division. The power to connect statistics, narratives and visualisations The list of users already reaping the benefits of the new connector is long. For instance, it is becoming easier and faster to access large amounts of official data on recorded COVID-19 cases or vaccinations and to build interactive monitoring dashboards. Data providers are now well equipped to provide easy-to-understand, regularly and automatically updated data and dashboards.
Source: Flow of Trade - Exports Between Countries, Pacific Community (SPC) International Merchandise Trade Statistics PowerBI Dashboard, 2021.
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These dashboards go beyond the reporting of numbers, allowing for detailed analyses tailored to users’ needs and for enhanced storytelling and sharing. Iulian Pogor, Data Architect and Statistical Tools Engineer at the International Labour Organisation, highlighted their use in telling compelling data stories, shedding light on the pandemic’s impact on the labour market, including unemployment, working conditions and unpaid work: “We can easily embed the visualisations on any website where the media and general public can interact with them and even print, download or share them in social networks.” Coming back to the opening question, we still have the same wealth of data. But we also have solutions to ensure that it is available to the right people, at the right time, in the right way. This includes policymakers, who need to make informed decisions, and policy shapers, notably the media, who structure and influence the public discourse. To sum up, statistical organisations can enable such data-driven conversations by providing better access, narratives and ways of sharing official statistics through the use of technologies such as Power BI.
About the SIS-CC The Statistical Information System Collaboration Community (SIS-CC) is a reference open source community for official statistics, focusing on product excellence and delivering concrete solutions to common problems through co-investment and co-innovation. The SIS-CC SDMX connector is an open-source project provided by the SIS-CC, developed in collaboration with Curbal AB, and certified by Microsoft.
Resources
> Webinar recording: SDMX Power BI Connector > > > >
launch Example on trade statistics: Power BI Dashboard Getting started I: Documentation page Getting started II: How to video How to contribute: Gitlab project site
Acknowledgements We would like to thank the following people for their contributions: David Barraclough, Smart Data Practices Manager at the OECD, for overseeing the implementation and certification process; Ruth Pozuelo Martinez, business owner of Curbal for collaboration and support on this project and certification with Microsoft; Phil Bright, GIS, Innovation and Dissemination Lead, Statistics for Development Division, SPC for contributing to the article with the use case and supporting the implementation through user testing; and Iulian Pogor, Data Architect & Statistical Tools Engineer, at the ILO for contributing to the testing of the connector.
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New platform for smarter financing for development data Sasha Ramirez-Hughes (sasha.ramirez-hughes@oecd.org), Paris21, Statistics and Data Directorate, OECD
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ast month’s UN World Data Forum was dominated by the lingering challenges of the COVID-19 pandemic and the need to get the world on track to achieve the Sustainable Development Goals (SDGs). The community was united and energetic in its commitment to work together and to ensure that all people benefit from the data revolution in addressing those challenges. That commitment is manifest in the global action plan of the High Level Group on Partnership, Coordination and Capacity-Building for statistics for the 2030 Agenda for Sustainable Development (HLG-PCCB), which calls for policy leaders to form a global pact or alliance recognising that funding modernisation efforts of national statistical offices are essential to the achievement of the 2030 Agenda, particularly in relation to low and middle-income countries. In this regard, according to the PARIS21 2021 Partner Report on Support to Statistics, two‑thirds of national statistical offices urgently need more funding to provide life‑saving data amidst the COVID‑19 pandemic, yet funding has not only stagnated but also become more fragmented.
To this end, the new Clearinghouse for Financing Development Data has emerged, alongside the World Bank’s Global Data Facility (GDF) and the UN Complex Risk Analytics Fund, each serving as key and complementary solutions recognised by the forum to catalyse the necessary step-change in development data finance to deliver Agenda 2030. The clearinghouse is the world’s first platform to track SDG data financing. Aid recipients, donors and others can use the free platform to analyse data financing flows, identify funding gaps, access data on over 35,000 projects, and connect to new communities of experts. The platform, developed by the Bern Network on Financing Data for Development, a multi-stakeholder alliance created in 2019 by the Swiss Government to promote more and better funding of development data, is intended as a mechanism to mobilise the global community into action towards achieving the SDGs by 2030.
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What can it tell us? Donors can use the clearinghouse to identify key data funding gaps in recipient countries, benchmark their country’s data funding, and highlight opportunities for joint projects with other donors. Recipient countries can use it to understand how much aid they are receiving for statistics across the board and plan investments accordingly, to assess their funding gaps to lobby for more resources from government and donors, and to determine best practices to improve the efficiency and effectiveness of investments in statistics and data. Researchers can analyse overall trends in financing for data and forecast future trends. Civil society organizations advocating for better data can review the funding landscape and identify who the top donors/ recipients are and which countries are increasing their own investment in data and statistics. How can it help? In a country like Malawi, highly dependent on agriculture and manual labour, the social costs of the pandemic are pressing. Indeed, a survey by the Institute of Public Opinion & Research in May 2020 found that “81% of Malawians feared going hungry during the COVID-19 pandemic, more than they feared being infected by the virus itself.” Yet, as with many countries, the pandemic, and measures to respond to it, have placed a considerable operational and financial burden on the National Statistics Office (NSO), while drastically increasing the demand for data.
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For instance, work on various surveys, such as the Multiple Indicator Cluster Survey and Fifth Integrated Household Survey, were halted or delayed, and it is becoming more difficult to mobilise resources for surveys such as the Malawi Demographic Health Survey. For Malawi to close statistical funding gaps, it needs comprehensive, granular data on the funding flows to, from and within the country for data and statistics. This information will help the government and the NSO to understand the scale and scope of the shortfall and identify areas for prioritisation. The government of Malawi expects that the clearinghouse will help it mobilise the resources necessary for data generation to meet the monitoring and evaluation requirements of the Malawi Growth and Development Strategy III as well as the country’s long term vision, called “Malawi 2063”, and the SDGs. Working with the Malawi NSO and government, the clearinghouse development team developed the country profile pages of the clearinghouse to show both inbound funding flows over time, as well as the current and future statistical budgets. This will enable Malawi to describe the size and nature of gaps, especially in the context of COVID-19. Or, consider gender equality. Accessible and timely sex-disaggregated data are critical in helping policymakers understand and address the unique challenges women and girls face. However, decades of low investment in building gender-sensitive
data systems continue to weaken our capacity to collect, analyse and use gender data effectively. But more financing alone is not enough to build gender data capacity. We need smarter financing to use scarce resources to their fullest potential. For countries to optimise their gender data investments and for donors to make smarter decisions on where to allocate their funds, we need easy access to up-to-date information on the state of gender data financing. The gender focus of the Clearinghouse for Financing Development Data addresses this need. This focus will help contextualise national and international gender data investments within the broader funding directed towards development data and statistics for the benefit
of development partners, civil society organisations and the public. These stakeholders can also use it to drill down to specific countries to identify the gaps in financing gender data and explore relevant capacitybuilding projects to better inform decision-making and bring projects to scale. Check out the Clearinghouse The Clearinghouse is available online and more resources and data are being added to it every day. Suggestions for or expressions of interest in new thematic channels, such as climate, food security or civil registration and vital statistics, are also welcome. Kindly contact PARIS21 for more information.
The 2021 OECD Compendium of Productivity Indicators The OECD Compendium of Productivity Indicators provides a set of cross-country comparable statistics on labour productivity levels and growth, as well as the contributions of labour, capital and multifactor productivity (MFP) to GDP growth. It also includes industry contributions to labour productivity growth, labour productivity gaps between SMEs and large firms, the evolution and composition of investment, the decoupling between real wages and productivity, and labour income share developments. The publication covers OECD countries, as well as other G20 economies whenever possible. While the focus of the report is on long-term productivity developments with data generally spanning up to 2019, the Compendium also discusses labour productivity evolutions in 2020 and the potential impact of the COVID-19 pandemic on productivity performance. It also reviews the main challenges brought by the pandemic for productivity measurement, e.g. for the measurement of non-market output and of capital input, and the cross-country comparability of labour statistics. From this year onwards, the Compendium is released as an online Webbook with interactive charts facilitating international comparisons, visualisations and the download of all productivity statistics commonly used for economic policy and analysis. OECD - Contributions to annual GDP growth: labour productivity, hours worked, and persons employed Total economy, percentage change at annual rate GDP per hour worked
Average hours worked per worker
Employment
GDP
3
2
1
0
-1 2000-2007
2007-2010
2010-2015
2015-2017
2017-2019 OECD
Source: OECD (2021), OECD Compendium of Productivity Indicators - Download chart.
Issue No. 75, December 2021 - The OECD Statistics Newsletter 15
A climate dashboard for effective climate action “Young people want to see [their leaders] making commitments, they want to see us taking action, and they want it to be measurable. Which is why we must monitor our progress on our commitments both rigorously and openly, and within a common framework. To this end, I am delighted that at COP26 the OECD is launching the International Programme for Climate Action, IPAC, which will enable us to track the results achieved, country-by-country, and see what remains to be done on the path ahead.” 01-11-2021, Glasgow, UK French President Emmanuel Macron’s remarks hailing the OECD’s initiative on climate action at the opening of the UN Climate Conference, COP26, echoed a widely held view: that no country had done enough to address climate change, and that public expectation for clear and measurable action remains high. The legally binding Paris Agreement, adopted at COP21 in December 2015, aims to limit global warming to well-below two degrees Celsius above pre-industrial temperatures, but in practice Nationally Determined Contributions (NDCs) are not yet ambitious enough to achieve these objectives according to the analysis of the UN and it is clear that much more effort is needed to cut greenhouse gas (GHG) emissions. Policymakers have been calling for international support and guidance to help navigate the road to net-zero in an economically resilient manner. How much action and when, what sectors, using which policies, at what cost: by answering such questions, countries can move forward with confidence. The new IPAC initiative can help. The IPAC Dashboard, with its interactive charts and clear data visualisations, is a central component of the IPAC established by the OECD Council in April 2021 and covers more than 50 countries, including the world’s biggest emitters, to measure and achieve progress in meeting their own climate goals. IPAC supports the UN climate process, and complements the UNFCCC and Paris Agreement monitoring efforts. Just as good data form the cornerstone of good policies, so analytically sound indicators drive the IPAC Dashboard. It draws on data from the OECD, the International Energy Agency (IEA), the International Transport Forum (ITF), the OECD Nuclear Energy Agency Different pathways to climate targets (NEA) and other international sources. The Dashboard focuses on greenhouse gas emissions in terms of trends, intensities and source sectors, the impacts, risks and vulnerabilities associated with climate change, and the actions taken by governments, including promoting innovation and pricing carbon. It includes interactive charts showing country trajectories towards their declared emission targets. The charts present the difference between the latest emission data and the target emission level in 2030, and the resulting trajectories, which differ by country depending on development levels and other factors, and should help policymakers consider the appropriate action.
Chile
Check out the preliminary Dashboard, which will be refined and expanded as new data and indicators become available. Feel free to explore and download the data and provide feedback to the IPAC team. Consult the first edition of the annual Climate Action Monitor, launched at COP26, which presents key insights building on the Dashboard, and examples of good practices.
United States
For more information, contact IPACinfo@oecd.org. For more detail on the methodology, see the Dashboard or contact Santaro Sakata. Source: OECD-IPAC Climate Dashboard, November 2021
16 The OECD Statistics Newsletter - Issue No. 75, December 2021
Issue No. 75, December 2021 - The OECD Statistics Newsletter 17
Recent publications Latin American Economic Outlook 2021 Working Together for a Better Recovery Latin American and the Caribbean (LAC) is the region most affected by the COVID-19 pandemic, and at risk of seeing the socio-economic gains of recent decades being reversed. Recovery strategies must comprise well-sequenced reforms that promote universal social protection systems, accelerate the formalisation of economies, improve fiscal progressivity, and deepen regional integration, says the Latin American Economic Outlook (LEO) 2021: Working Together for a Better Recovery. According to this 14th edition of the report, LAC experienced a historical economic downturn in 2020. The region’s gross domestic product (GDP) contracted by around 7.0%. Despite a rebound of around 6.0% in 2021, its GDP per capita is not expected to return to pre‑crisis levels before 2023‑24. The impact of the crisis has been asymmetric, particularly affecting the most vulnerable groups. As a result, poverty and extreme poverty levels are at their highest in 20 and 12 years, respectively. OECD et al. (2021), Latin American Economic Outlook 2021: Working Together for a Better Recovery, OECD Publishing, Paris - https://www.oecd.org/dev/latin-american-economic-outlook-20725140.htm
OECD Economic Outlook, Volume 2021 Issue 2 The global recovery is continuing but its momentum has eased and is becoming increasingly imbalanced according to the OECD’s latest Economic Outlook. The failure to ensure rapid and effective vaccination everywhere is proving costly with uncertainty remaining high due to the continued emergence of new variants of the virus. Output in most OECD countries has now surpassed where it was in late-2019 and is gradually returning to the path expected before the pandemic. However, lower-income economies, particularly ones where vaccination rates against COVID-19 are still low, are at risk of being left behind.. OECD (2021), OECD Economic Outlook, Volume 2021 Issue 2, OECD Publishing, Paris https://oecd.org/economic-outlook
IEA World Energy Outlook 2021 Against the backdrop of turbulent markets and a crucial meeting of the COP26 conference on climate change in Glasgow, the 2021 World Energy Outlook (WEO) provides an indispensable guide to the opportunities, benefits and risks ahead at this vital moment for clean energy transitions. The WEO is the energy world’s most authoritative source of analysis and projections. This flagship publication of the IEA has appeared every year since 1998. Its objective data and dispassionate analysis provide critical insights into global energy supply and demand in different scenarios and the implications for energy security, climate targets and economic development. IEA (2021), World Energy Outlook 2021, OECD Publishing, Paris. https://www.iea.org/reports/world-energy-outlook-2021
18 The OECD Statistics Newsletter - Issue No. 75, December 2021
Forthcoming meetings Unless otherwise indicated attendance at OECD meetings and working parties is by invitation only.
OECD Date
Meeting
13-15 December 2021
Working Party of National Experts on Science and Technology Indicators (NESTI), Directorate for Science, Technology and Innovation, OECD Task Force on Insurance Statistics, Directorate for Financial and Enterprise Affairs, OECD DAC Working Party on Development Finance Statistics (WP-STAT), Development Co-operation Directorate, OECD Working Party on International Trade in Goods and Services Statistics (WPTGS), Statistics and Data Directorate, OECD Working Group on International Investment Statistics (WGIIS), Working Group on International Investment Statistics (WGIIS) Working Party of National Experts on Science and Technology Indicators (NESTI), Directorate for Science, Technology and Innovation, OECD Working Party on Indicators of Educational Systems (INES), Directorate for Education and Skills, OECD DAC Working Party on Development Finance Statistics (WP-STAT), Development Co-operation Directorate, OECD 2nd Workshop on Time Series Methods for Official Statistics, Statistics and Data Directorate, OECD 5th session of the Working Party of Tourism Statistics, Centre for Entrepreneurship, SMEs, Regions and Cities, OECD Working Party on Measurement and Analysis of the Digital Economy (WPMADE), Directorate for Science, Technology and Innovation, OECD 8th International Transport statistics, International Transport Forum:
15 December 2021 16-18 March 2022 21-23 March 2022 22-24 March 2022 23-25 March 2022 23-25 March 2022 23-25 March 24-25 March 2022 5-6 April 2022 11-12 April 2022 15 April 2022 11 May 2022 16 May 2022 20-24 June 2022 22-24 June 2022
Working Party on Territorial Indicators (WPTI), Centre for Entrepreneurship, SMEs, Regions and Cities, OECD 8th Meeting of the Working Party for the OECD Patient Reported Indicator Surveys (PaRIS), Directorate for Science, Technology and Innovation, OECD DAC Working Party on Development Finance Statistics (WP-STAT), Development Co-operation Directorate, OECD Committee on Statistics and Statistical Policy (CSSP), Statistics and Data Directorate, OECD
26-27 July 2022
Working Group on International Investment Statistics (WGIIS) - Interim meetings, Directorate for Financial and Enterprise Affairs, OECD 12-14 September 2022 Working Party of National Experts on Science and Technology Indicators (NESTI), Directorate for Science, Technology and Innovation, OECD 19-21 October 2022 Working Party on Indicators of Educational Systems (INES), Directorate for Education and Skills, OECD 24-28 October 2022 Working Party on International Trade in Goodsand Services Statistics (WPTGS), Statistics and Data Directorate, OECD 24-28 October 2022 Working Party on National Accounts (WPNA), Statistics and Data Directorate, OECD 2-9 November 2022 7 November 2022
Expert Group on Extended Supply and Use Tables / Trade in Value Added, Statistics and Data Directorate, OECD 9th Meeting of the Working Party for the OECD Patient Reported Indicator Surveys (PaRIS), Directorate for Employment, Labour and Social Affairs, OECD
Other meetings 17-21 January 2022 22-24 April 2022
World Economic Forum Annual Meeting Spring Meeting of the World Bank Group and the International Monetary Fund
Issue No. 75, December 2021 - The OECD Statistics Newsletter 19
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