OECD Statistics Newsletter, Issue 65, November 2016

The Statistics Newsletter For the ex tended OECD s tatis tic al net work

FEATURING ++Value of Official Statistics ++2015 OECD Frascati Manual: gaining new insights on R&D ++Official statistics & mobile network operator partner up

THE LATEST GOOD STATISTICAL PRACTICE TOOLKIT NEW WEBSITE

www.oecd.org/std/statisticsnewsletter Issue No. 65, November 2016

ENTREPRENEURSHIP AT A GLANCE 2016

Contents 3

Value of Official Statistics: Convincing our stakeholders, measuring value of statistics

Anu Peltola (anu.peltola@unece.org), United Nations Economic Commission for Europe, Eoin McCuirc (eoin.mccuirc@cso.ie), Central Statistics Office Ireland and Peter van de Ven (peter.vandeven@oecd.org), OECD Statistics Directorate; Members of the UNECE Task Force on the Value of Official Statistics

6

Implementing the 2015 OECD Frascati Manual to gain new insights on R&D

Daniel Ker (daniel.ker@oecd.org) and Fernando Galindo-Rueda (fernando.galindo-rueda@oecd.org), Directorate for Science, Technology, and Innovation, OECD

11

Official statistics and mobile network operator partner up in Belgium

Marc Debusschere (marc.debusschere@economie.fgov.be), Statistics Belgium, Jan Sonck (jan.sonck@ proximus.com), Proximus and Michail Skaliotis (michail.skaliotis@ec.europa.eu), Eurostat

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Estimating the Transport and Insurance Costs of International Trade

Guannan Miao (guannan.miao@oecd.org) and Fabienne Fortanier (fabienne.fortanier@oecd.org), Statistics Directorate, OECD

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Measuring Support to Statistics in Developing Countries

Johannes Jütting (johannes.jutting@oecd.org) and Thilo Klein (thilo.klein@oecd.org), PARIS21

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Forthcoming meetings

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Recent publications

The Statistics Newsletter is published by the OECD Statistics Directorate.

This issue and previous issues can be downloaded from www.oecd.org/std/statisticsnewsletter To receive the OECD Statistics Newsletter by email, you can subscribe to OECDdirect e-mails: www. oecd.org/about/publishing/oecddirect.htm @OECD_STAT Editor-in-Chief: Martine Durand Editors: Nadim Ahmad and Peter van de Ven Editorial and technical support: Sonia Primot and Martine Zaïda For further information or to send articles please contact: std.statnews@oecd.org Deadline for articles for the next issue: 30 March 2017

2 The OECD Statistics Newsletter - Issue No. 65, November 2016

Value of Official Statistics Convincing our stakeholders, measuring value of statistics Anu Peltola (anu.peltola@unece.org), United Nations Economic Commission for Europe, Eoin McCuirc (eoin.mccuirc@cso.ie), Central Statistics Office Ireland and Peter van de Ven (peter.vandeven@oecd.org), OECD Statistics Directorate; Members of the UNECE Task Force on the Value of Official Statistics

Official statistics in the midst of data redundancy

S

tatisticians measure almost every aspect of society, but not their own value added.

Official statistics have been a success story over decades, but in the new world of big data this is not that self-evident anymore. In March 2015, heads of national statistical offices decided that there is a need to consider the strategic positioning of official statistics. The Conference of European Statisticians (CES) established a Task Force on the Value of Official Statistics (TF) to define what users, stakeholders and society value in official statistics; to develop ways to measure, and to increase this value; and to communicate it more effectively.

Data are available in unimaginable quantities. Around 90% of existing data have been created in the past few years. According to estimates, the amount of data is doubling every two years. The abundance of data seems to be inflating the value of other information, and official statistics are at risk of being buried under piles and piles of other data. In these times, quality should become the decisive factor when choosing a data source, and it is the unique quality of official statistics that we should be able to communicate better.

•• Technological advances have powered the Digital and Data Revolutions. These advances raise legitimate questions about how effectively official statisticians are using the new possibilities.

•• Big Data – its uncertainty, complexity, velocity and size – challenges the expertise of statistical offices.

They need to improve their capacities for using huge amounts of data that come in varying formats.

•• Demand for statistics is rapidly increasing.

The reporting on progress towards the Sustainable Development Goals (SDGs) alone requires hundreds of indicators to be produced and will require establishing many new partnerships.

•• Users’ needs are becoming more complex and individualised. The public demands easy answers to their questions and ask for more tailored products that are easy to use.

Official statistics are based on values enshrined in the UN Fundamental Principles of Official Statistics, which apply to everything we do: •• We are impartial: we publish relevant findings without fear or favour.

•• We are professional: we have rigorous quality assurance practices.

The TF builds on the UN Fundamental Principles of Official Statistics (www.unece.org/stats/archive/docs. fp.e.html) (see box),1 developed by the CES in 1991, as well as on the Recommendation of the OECD Council on Good Statistical Practice2 and the European Statistics Code of Practice.3 At the same time, the TF recognised new challenges and opportunities:

•• We are scientific: we facilitate a correct interpretation of data by using scientific standards.

•• We are vocal: we provide information on the use of our statistics and interpretation of our statistics.

•• We are flexible: we draw information from many sources.

•• We protect confidentiality: we operate in secure physical and digital environments.

•• We are transparent: we fully disclose our methods and

•• Tightening budgets increase the pressure to demonstrate how effectively official statisticians use public funds to meet users’ needs.

standards.

•• We collaborate: we work with statistical agencies within our country to uphold a consistent and efficient statistical system.

•• We promote efficiency: we continually review and update our

•• National statistical offices are in tight competition with the information industry that provides fast and easy access to data in user-friendly formats. Official statisticians should learn from their innovations.

methods, processes and systems.

•• We are global: we cooperate with international partners to ensure best methods.

Issue No. 65, November 2016 - The OECD Statistics Newsletter 3

International work on value of official statistics The TF is composed of representatives from the United Kingdom (chair), Australia, Canada, Ireland, Mexico, New Zealand, Switzerland and Turkey as well as Eurostat, OECD, PARIS21, World Bank and UNECE (secretariat). Its main objectives are to shed light on: •• What is the business of official statistics in the midst of the Data Revolution?

•• What are the main reasons for making investments in official statistics?

•• How to share best practices more effectively among statistical offices?

•• How to explain the value of official statistics better?

Eight recommendations

1. Exploit the comparative advantage of official statistics: Continue to rely and build on the cornerstones of official statistics – professional independence, scientific methods, rigorous quality criteria and the UN Fundamental Principles.

One of the strongest motivations for producing information is its usefulness for evidence-based decision 2. Put customers truly at the centre: Listen to users making. Official statistics are arguably of little value and be user-centred in everything that is being in themselves unless they help make done. Data is not the whole story – it well-based decisions and provide the The value depends needs context. There is a need to move public with equal opportunities to access away from bulk data provision towards on changing information. higher value products. Nowadays official circumstances and statisticians cannot simply say “no” to new needs.

The value depends on changing demands. circumstances and needs. Oil lamps lost value first after the advent of gas and then after electric 3. Design statistics for everyday life: Design is much lighting became available. Similarly, the value of official more than logos and graphics – it is about keeping statistics has to be developed continuously. customers engaged. Private businesses integrate data into devices, apps and analytical tools. Statistical To come up with the right recommended actions and data could be used in a similar way as part of people’s collate innovative practices, a survey of statistical offices daily lives. was carried out in October 2015. A wiki platform was created to showcase about 150-200 best practices 4. Innovate to remain valuable: Be more agile to keep (www1.unece.org/stat/platform/x/FQRXBw). up with changing user demands. Even though regular statistical production requires time and resources, The analysis of how other industries create, measure enough time should be allocated to thinking about how and communicate their value brought up some recurring to unleash the potential of statistics to improve lives. features defined as dimensions of value (see the graph).

If a business doesn’t provide value to its customers, it 5. Further engage with strategic partners: Strong will cease to exist. Even though official statistics are not partnership exists within the official statistical a private good, they are produced in an increasingly community. Partnerships with the private sector, competitive environment where official statisticians however, represent largely untapped potential. need to prove their relevance continuously. Similarly, Partnerships may enable access to new data, businesses nowadays need to demonstrate their positive technologies, knowledge, ideas, dissemination impacts on society. channels and networks.

Based on the above survey and further discussion, the TF came up with eight recommendations:

4 The OECD Statistics Newsletter - Issue No. 65, November 2016

6. Build the official statistics brand and gain visibility: “Hiding our light under a bushel” is not a

virtue. The value added of official statistics need to be demonstrated better and more vigorously.

7. Measure outcomes to achieve greater impact: If one can improve the knowledge of what official statistics are used for, and the impact they can have, one will be better able to prove their value added. The economic value of official statistics and the users’ views on their value should be measured to the extent possible.

8. Share and learn using a plug-and play platform: A best practices wiki platform is in the process of being finalised. This platform will provide a plug and play platform to help learn from other countries’ best practices for each of the above recommendations.

When the wiki is ready, countries will be invited to use it for learning, and to further enhance it with posting new innovative cases.

How to measure the value of official statistics?

Statistical offices already monitor the value added of statistics through citations in the media, number of downloads and user satisfaction. Some go further by calculating a composite indicator of public interest in official statistics and services, or by publishing a recommendation index to monitor the share of users that would recommend official statistics to others.

A framework to measure the value of official statistics may include two types of indicators: •• Observable indicators could form a dashboard of indicators, including the number of citations, with information that is available from existing sources.

•• Subjective indicators that are typically being derived from user satisfaction surveys. A generic user survey, developed by the TF, will be pilot tested this autumn

by Ireland and Mexico.

Further, the OECD leads the TF’s work on potential methodologies to monetise the value of statistics. Five approaches are being considered: 1. Cost or investment approach uses the production costs or investments needed for statistical production.

2. Market (equivalent) pricing uses the market prices charged by commercial data providers as a starting point.

3. Stated preference methods derive estimates from surveying the “willingness to pay” of users of official statistics.

4. Revealed preference methods try to estimate the monetary value of statistics by deriving information from actually observed consumer preferences.

5. Impact assessments aim to assess the causal (monetary) effects of the availability of statistics on economic and social outcomes.

Not only recommendations – but also tools To summarise, the UNECE TF is currently working on the finalisation of the following tools: •• Measurement framework with objective and subjective indicators and methodologies for monetising the value of official statistics.

•• Convincing points on the value added of official statistics that could be used in stakeholder collaboration.

•• Generic user surveys to measure users’ views on the value of official statistics.

•• Best practices wiki platform linked to the recommendations on the value of official statistics for plug and play.

The report Recommendations on Value of Official Statistics will be finalised in June 2017 for a discussion among the Heads of statistical offices, and for subsequent implementation. The TF’s interim report was published in February 2016 (www.unece.org/fileadmin/DAM/ stats/documents/ece/ces/2016/mtg/CES_11_-ENG_ G1602756.pdf).

1. Based on the Modernstats video www.youtube.com/watch?v=uxb3iOnVr1Y and Statistics Canada’s related promotional video.

2. www.oecd.org/statistics/good-practice-toolkit/Brochure-Good-Stat-Practices.pdf 3. http://ec.europa.eu/eurostat/web/quality/european-statistics-code-of-practice

Issue No. 65, November 2016 - The OECD Statistics Newsletter 5

Implementing the 2015 OECD Frascati Manual to gain new insights on R&D Daniel Ker (daniel.ker@oecd.org) and Fernando Galindo-Rueda (fernando.galindo-rueda@oecd.org), Directorate for Science, Technology, and Innovation, OECD

T

he Frascati Manual (FM) underpins the ongoing, coordinated Research and Development (R&D) data collections of the OECD, Eurostat, and UNESCO. Maintained by the OECD Working Party of National Experts on Science and Technology Indicators (NESTI), it fuels the OECD Research and Development Statistics database (www.oecd.org/sti/rds) and Main Science and Technology Indicators publication (www.oecd.org/sti/ msti.htm).

The FM defines the conceptual basis for widely-used statistical series of high policy relevance including “Business Expenditure on R&D” (BERD), “Gross Domestic Expenditure on R&D” (GERD), the GERD to GDP ratio (Figure 1), and counts of researchers – with the latter two being used to measure progress on United Nations Sustainable Development Goal no. 9 (infrastructure and innovation).

In an effort to better incorporate the “knowledge economy” into economic statistics, the 2008 System of National Accounts (SNA) recognised R&D as a produced

asset and adopted FM definitions of R&D. The existence of established and comparable R&D data via the FM data collection, as well as a process of close cooperation between FM and SNA practitioners (Canberra II) enabled this change – marking the entrance of FM R&D data into the statistical “mainstream”.

What’s new in the 2015 Frascati Manual?

The 7th edition (http://oe.cd/frascati), entitled “Frascati Manual 2015: guidelines for collecting and reporting data on research and experimental development” (FM2015) builds on the foundations of earlier editions (the first of which was published in 1963), while proposing a number of changes and new data collections which should offer improvements to existing data and new insights for economies at all levels of economic development.

The main novelties are listed below, with further explanation in the following sections: •• Integrates ad hoc guidance on measuring R&D in developing countries into the core manual;

Figure 1. Gross Domestic Expenditure on R&D (GERD) as a percentage of Gross Domestic Product (GDP) 2014 or latest available % 4.5 4.0 3.5 3.0 2.5 2.0 1.5 1.0 0.5 0.0

Source: OECD Main Science and Technology Indicators, June 2016 - http://oe.cd/msti

6 The OECD Statistics Newsletter - Issue No. 65, November 2016

•• Extends guidance on the use of surveys to other sources, such as administrative records; •• Goes beyond the use of R&D data for macro indicators and conceived to facilitate the production and use of R&D microdata for the analysis of R&D dynamics and impacts; •• Addresses the role of external contributors (e.g. selfemployed, volunteers, and students);

A key accompanying innovation is that 5 defining features of R&D activities are identified. To be counted as R&D, an activity must be all of the below: •• novel – aimed at new findings; •• creative – based on original, unobvious, concepts and hypotheses; •• uncertain – the final outcome is not known beforehand;

•• Contains four new, sector-specific chapters on practical issues arising in data collection;

•• systematic – planned and budgeted; and •• transferable/reproducible – the approach and findings can be communicated and reproduced.

•• Provides new guidance on measuring R&D globalisation in business and elsewhere; •• Describes different types of public support for R&D and includes guidance on how to measure the cost of R&D tax incentives for the first time; •• Aligns definitions and classification schemes with general statistical guidance while still maintaining key R&D-specific classifications;

OECD (2015), Frascati Manual 2015: Guidelines for Collecting and Reporting Data on Research and Experimental Development, OECD Publishing, Paris. http://dx.doi. org/10.1787/9789264239012-en

•• Facilitates the new (2008 SNA) treatment of R&D as capital investment in the National Accounts; and •• Includes specific guidance on improving the timeliness of key indicators.

Improved clarity about what is – and what is not – R&D In recognition of the need to preserve long time series and the fact that Frascati definitions are used in the System of National Accounts and wider contexts (e.g. tax rules), FM2015 introduced only minor changes (aimed at ensuring clarity and inclusiveness) to the definition of R&D: Research and experimental development (R&D) comprise creative and systematic work undertaken in order to increase the stock of knowledge – including knowledge of humankind, culture, and society – and to devise new applications of that knowledge.

These core criteria are designed to help compilers of R&D statistics and survey respondents to consistently delineate R&D and non-R&D activities, boosting the international comparability of R&D statistics. Because it is also used by many countries as the legal reference document for grant and tax support purposes, this clarity in FM2015 may also directly contribute to the better use of public funds.

Better understanding of the organisations and people doing R&D Like the National Accounts, FM2015 has adopted the “institutional unit” to aggregate reporting units, with the two classifications fully aligning, except for Higher Education Institutions (HEIs), which are a separate sector within the FM framework.

FM2015 is also now fully aligned to the definition of “residence”, which determines whether a unit should be included in domestic statistics or in the “rest of the world” sector, and further encourages the use of statistical business registers as a basis for business R&D surveys.

Such changes increase the compatibility of R&D data with other statistical frameworks at different levels: at the unit level, the use of statistical business registers facilitates linking data from different sources (i.e. identifying the same units within R&D and other economic surveys) – the use of R&D microdata is strongly encouraged in the manual; and

Issue No. 65, November 2016 - The OECD Statistics Newsletter 7

•• at the aggregate level, totals, except for HEIs, are perfectly comparable with the National Accounts Framework of institutional sectors.

A wealth of new insights into the economic performance of the organisations and sectors which undertake R&D should result from this improved compatibility.

Greater understanding of relationships between actors in the R&D system The FM first came into existence during an era of large industrial corporations and its recommendations reflected the way such companies undertook R&D. While R&D and innovation have become more open and distributed, the conceptual framework and its focus on R&D activity under the responsibility of the statistical unit continues to prove useful in a world where, although knowledge and Intellectual Property (IP) rights can be easily relocated, significant policy interest in where the substantive activity takes place (“intramural R&D”) remains.

The FM provides instruments for capturing R&D flows, advocating the collection of R&D funding data by source sector (e.g. own funds, government funds, other business funds, funds from the rest of the world, etc.).

The aforementioned adoption of institutional units for analysis strengthens this by clarifying the boundary between internal and external funding. A significant enhancement comes from the FM2015 recommendation

that funding flows should also be broken down by the type of the funding: •• “Purchases” of R&D by the funder – with the payment being requited through provision of R&D services (reports and presentations of results, findings, etc.).

Known formally as “exchange funding” due to the exchange of money for R&D services, an example would be a business or government unit buying R&D services from a specialist research laboratory.

•• “Grants” or other forms of funding where money is given with the express intention of funding R&D performed by the recipient and no direct compensation or IP are expected/given in return. These represent “transfer funding” given to the R&D performer.

This new information promises to deepen the understanding of the different drivers of R&D, indicating the relative importance of commercial contracts, grants and subsidies, charitable donations, and self-funding.

This information is of broad policy interest and also offers important insights into the ownership of R&D assets recorded in the National Accounts.

This approach complements the new chapter on “measurement of government tax relief for R&D” (chapter 13), which sets out a framework for measuring R&D support by governments through provisions that allow businesses to forego corporation tax payments (e.g.

Figure 2. Direct government funding of business R&D and R&D tax incentives – as a percentage of GDP 2014 or latest available % 0.45

Indirect government support through tax incentives

Direct government funding of BERD

Data on tax incentive support not available

0.40 0.35 0.30 0.25 0.20 0.15 0.10 0.05 0.00

Source: OECD, R&D Tax Incentive Indicators, http://oe.cd/rdtax and Main Science and Technology Indicators, www.oecd.org/sti/msti.htm, July 2016.

8 The OECD Statistics Newsletter - Issue No. 65, November 2016

through tax credits, enhanced allowances, accelerated depreciation allowances, etc.). It completes the picture to give a comprehensive view of how budgetary and administrative data can be used to better understand public support for R&D – see Figure 2.

The improved understanding of relationships between agents in the R&D system also extends to the international context, with Chapter 11 of the new manual laying out a detailed conceptual framework for the “measurement of R&D globalisation” – particularly within multi-national enterprises (MNEs). The OECD proposes to collect Business Expenditure on R&D (BERD) broken down by respondents’ MNE status (non-MNE, subsidiary of a foreign unit, domestic unit with subsidiary unit(s) overseas), and further information on funding of domestic HEI R&D by HEI units overseas (branch campuses abroad, other non-affiliated HEIs).

The FM is not only about financial resources but also about the people who undertake R&D. Chapter 10 focuses on non-profit organisations and individuals in R&D efforts. Furthermore, FM2015 reviews recommendations on the measurement of human resources dedicated to R&D within organisations and provides a clearer delineation between “internal R&D personnel” – paid employees working on R&D – and “external R&D personnel” – consisting of consultants hired directly or through intermediate firms as well as certain doctoral/masters students, R&D grant holders, professors emeritus, and/or volunteer contributors. It is also proposed that government and HEI researcher headcounts be split by seniority within the organisation.

Together these changes will help to strengthen analyses.

Reflecting relevant statistical developments In addition to classifying units into institutional sectors, FM2015 extends the cross-classification of units by main activity (in accordance with the International Standard Industrial Classification, ISIC Rev 4) to units in all sectors (i.e. government, and Private Non-Profit units as well as businesses). This will offer an improved view of the links between R&D and other activities across the whole economy.

FM2015 also specifically encourages the separate identification of expenditures on R&D undertaken in the course of developing software. A major reason for this is that software development will often involve considerable R&D and both in-house R&D and in-house software development are typically valued indirectly by summing input costs.

However, while FM R&D expenditure measures include all R&D costs (including the costs of R&D undertaken in the course of developing software), the National Accounts records at least some of these expenditures as software investment and not R&D. The implication is that a significant portion of FM R&D expenditures may be excluded from National Accounts R&D estimates and instead recorded as software.

At this time, few countries have published separate R&D output and/or investment (Gross Fixed Capital Formation) series, choosing instead to leave them aggregated within the broader category of “intellectual property products”. When these figures become available, users should be cognisant of the differing treatment of capital expenditures and expenditures on R&D undertaken in the course of developing software when comparing with FM expenditure series such as BERD and GERD totals.

It should also be noted that this change to National Accounting practice has added as much as 3% to the level of GDP in some countries. These increases in GDP, all other things being equal, reduce key ratios such as BERD/GDP and GERD/GDP, which should be considered when interpreting them (e.g. when assessing the achievement of policy targets), as discussed in the OECD Estimates of R&D Expenditure Growth in 2012 note available at http://oe.cd/1zz.

FM2015 also acknowledges the increasing relevance of administrative data sources – which are becoming increasing available (and sometimes on a more timely basis) – occurring in the face of falling survey response rates and the relative costliness of surveys. While dedicated surveys are in most cases better targeted instruments, the complementary use of administrative data is encouraged and OECD is keen to provide a platform for sharing experiences with R&D administrative and budgetary data in the hope of identifying best practice.

Improving relevance The 2015 Frascati Manual proposes a range of changes to R&D collections at the national level, aimed at providing new, relevant insights to policy makers and analysts.

Some compilers of R&D statistics have already made changes reflecting the new manual but implementation is only just beginning in most countries. It is expected that the OECD will be able to make FM2015 data available from 2018.

As an important first step, the OECD and Eurostat are consulting member countries on proposed new FM

Issue No. 65, November 2016 - The OECD Statistics Newsletter 9

2015-based versions of the coordinated questionnaires used to gather R&D data. The questionnaires include the key changes outlined above such as: splitting R&D funding flows by source sectors and type of funding; collecting GERD broken down by (simplified) main activity; and information on R&D conducted within MNEs. For the first time, it is also proposed to collect relevant structural information including counts of R&D performing businesses by sector and number of employees as well as the concentration of business sector R&D expenditure and personnel to offer valuable context to the main R&D indicators. Specific attention is also given to the distinction between public and private units within the business enterprise and higher education sectors – with units being defined as “public” on the basis of government control criteria used in the National Accounts.

The separate identification of women within R&D personnel and across other dimensions (such as qualification, age, field of R&D, region, etc.) also lends full support to the Beijing Declaration following the 1995 World Conference on Women (United Nations, 1995).

A crucial next step will be for those conducting R&D surveys and compiling R&D statistics to translate those

changes into their own data collections. At this time, user engagement can help ensure that the areas of greatest user relevance are prioritised for implementation.

To assist in this process, compilers of R&D data can draw upon a series of online resources and advice from the entire community compiled and managed by the OECD. Implementation workshops have already been held in Seoul and Tokyo; others can be planned on request.

The OECD will also continue to enhance the FM2015 by producing/updating online annex materials – including updated guidance on R&D price indices that build on the significant work of National Accountants in the course of capitalising R&D. NESTI also intends to devote efforts to considering recommendations for collecting information on potential outputs of R&D once the on-going revision of the Oslo Manual (on measuring innovation) is complete.

Future extensions will be planned on the basis of user feedback and implementation practices.

User feedback is welcomed on the new Frascati Manual, the implementation process, and OECD R&D statistics databases and publications. Please contact RDSurvey@oecd.org.

Entrepreneurship at a Glance 2016 Exploring evidence from a new online monthly business survey

OECD (2016), Entrepreneurship at a Glance 2016, OECD Publishing, Paris. http://dx.doi.org/10.1787/entrepreneur_aag-2016-en

The 2016 edition of Entrepreneurship at a Glance (http://oe.cd/ EaaG) introduces for the first time data from a new online monthly business survey, the Future of Business Survey, designed by Facebook in collaboration with the OECD Statistics Directorate and the World Bank (www.futureofbusinesssurvey.org). The survey aims to generate timely information on businesses’ sentiment about their current state and outlook as well as main challenges faced, along with relevant statistics on the characteristics of the respondents (in particular firm age and size, gender of the top management or ownership, participation in international trade, and use of online tools). The target population are enterprises with a digital exposure, e.g. firms with a Facebook Page.

The new survey, which is run on a monthly basis in 22 OECD and partner countries, was launched in February 2016. Data gathered from the Future of Business Survey usefully complement the established harmonised series of official statistics on businesses compiled by NSOs which are available with a delay of two to three years. While work is ongoing to improve the sampling and extend the country coverage, the analysis of the data collected during the first seven rounds of the survey are encouraging with regards to data quality and reliability.

10 The OECD Statistics Newsletter - Issue No. 65, November 2016

Official statistics and mobile network operator partner up in Belgium Marc Debusschere (marc.debusschere@economie.fgov.be), Statistics Belgium, Jan Sonck (jan.sonck@proximus.com), Proximus and Michail Skaliotis (michail.skaliotis@ec.europa.eu), Eurostat

C

hanges in society and technology have recently given rise to ‘big data’, the explosive increase of huge volumes of unstructured data being created continuously by sensors and cameras, satellites, machineto-machine communication, e-business, electronic payments and withdrawals, all kinds of internet activity, social media, etc. This data deluge contains valuable information which may be used for operational and commercial purposes, but also to produce official statistics.

when they start to commercialise their data. From the perspective of their core business of running a mobile network, these data are ‘exhaust’ and operators often fail to realise that data cannot speak for themselves, and that turning them into valid and accurate information requires skills they often lack. Another drawback is that operators possess only mobile phone data, with limitations that soon become apparent.

Of course, the fact that both statistical institutes and network operators encounter challenges which can only be addressed collectively, provides a solid foundation for mutually advantageous cooperation.

Mobile phone data represent a potentially promising big data source for statistics, most notably in the domains of population, migration, tourism and mobility. Exploiting A collaboration project them is expected to result in faster and even real-time statistics, at a much more detailed level, with nearly Statistics Belgium, Eurostat and Belgium’s leading1 complete coverage, eliminating response bias, at a lower network operator Proximus decided in December cost, and without the need to burden 2015 to launch a project for the joint This data deluge citizens and enterprises. Moreover, exploration of the Proximus mobile they offer an entry point in near-real phone data in order to assess their contains valuable time to phenomena inaccessible until information content and possible uses. information which now (e.g. actually present as opposed While the scope was deliberately left may be used for to registered population, detailed open at the start of the project (because operational and commuting patterns by day of the week, of its exploratory character), there commercial purposes, was nonetheless a clear ambition of weather conditions, etc.). but also to produce presenting the first concrete results by However, access and use of these April 2016; consequently, the project official statistics. data is hampered by several factors. team agreed on concrete research Statistical institutes do not own the data and therefore objectives. The project group, soon joined by the have limited knowledge about their characteristics, they European Commission’s Joint Research Centre (JRC), lack the IT infrastructure for handling the large volumes assembled 11 specialists covering all possible angles, involved, and legal arrangements to regulate access contributing technical, statistical, data warehouse, IT, and use are inadequate. Moreover, compelling network geographic information system (GIS), business and operators to provide access is problematic because domain expertise. mobile phone data are not just lying about, ready to be used by official statistics. On the contrary, significant The project was designed to follow a logical and prior investment is needed to transform network signals incremental order, starting with the estimation of actual and events into exploitable data. population, then resident population (establishing usual place of residence), determining place of work and all Fortunately, most network operators are now aware they other aspects of the ‘usual environment’, finally leading to also need these data for their own purposes and have the possibility of measuring commuting, tourism, shortstarted investing. Firstly for network optimisation, but a term and longer-term labour mobility and migration. growing number of operators also see the commercial In order to avoid all privacy issues and the delay they potential of the data they hold. The initial investment, could cause, the first studies used only aggregated data, however, is not the only challenge operators are facing without any tracking of individual mobile devices.

Issue No. 65, November 2016 - The OECD Statistics Newsletter 11

Labour Productivity in 2015

G7 countries, GDP per hour worked, USD dollars, current PPPs

$

$

$

$

$

$

68.3

$

$

$

$

$

$

65.6

$

$

$

$

$

$

65.5

$

$

$

$

$

52.1

$

$

$

$

$

51.9

$

$

$

$

$

50.9

$

$

$

$

$

49.4

$

$

$

$

OECD (2016), OECD Productivity Statistics (database)

42.1

$

Level of GDP per capita and productivity http://stats.oecd.org/Index. aspx?DataSetCode=PDB_LV

OECD Statistical Insights

Key findings

Interested in the story behind the indicator? Check out the OECD Statistical Insights blog series (http://oecdinsights. org/category/statinsights) that focuses on specific indicators and the key findings they illustrate. Since launching the series in early 2016, a number of stories have already been told, including:

Data

•• New OECD database on International Transport and Insurance Costs

Evidence

•• What does GDP per capita tell us about households’ material well-being?

Indicators

Method

•• Job strain affects four out of ten European workers •• Who’s Who in International Trade: A Spotlight on OECD Trade by Enterprise Characteristics data •• Government assets matter too, not just debt

12 The OECD Statistics Newsletter - Issue No. 65, November 2016

Figure 1. Population density per km² based on mobile phone data (left) and 2011 Census (right)

The first overall analysis, of actual and resident population and population density, was innovative in several important ways. First of all, unlike most previous studies using call detail records (CDRs), used for billing whenever a mobile phone is used to locate a device in space and time, this study is based on all network signalling events, about 10 times higher than the number of CDRs. The analysis also combines mobile phone data with statistical datasets which makes it possible to validate one against the other and create totally new information. In order to do so, the geographical units of mobile phone data (the area covered by each of the 11,000 mobile network cells) have to be converted in the standard 1 km² area of statistical datasets, and vice versa (much higher precision is foreseen in future studies).

First results were completed and published as foreseen2 (De Meersman, Seynaeve, Debusschere e.a., 2016).

They can be highlighted by two graphs. The first one (Figure 1) shows population densities derived from mobile phone counts at 4 a.m. on Thursday 8 October (left) and the 2011 population census (right). The Pearson correlation between these two datasets is 0.85, a clear indication that mobile phone data are able to provide a valid and accurate measure of population density.

Figure 2 shows the results of a cluster analysis of the weekday observations, with a typical ‘residential’ pattern (mobile phones leaving in the morning and returning in the evening), ‘work’ pattern (the other way around) and ‘commuting’ pattern (two rush hour peaks); mapping these makes perfect sense.

The project is ongoing and now focuses on the addition of spatiotemporal datasets (e.g. land use, urban versus rural areas) which should help filter out error variation

Figure 2. Weekday cells identified as ‘residential’, ‘commuting’ or ‘work’, with mapping

Issue No. 65, November 2016 - The OECD Statistics Newsletter 13

and consequently increase correlations, and on new sets of mobile phone data to tackle further questions.

The emerging business model The close collaboration between a statistical institute and a mobile network operator has proven in practice that more value can be created together than separately.

The advantages for official statistics are obvious: without access to the data there simply can be no statistics, but the metadata, technical expertise, data storage and processing infrastructure, and use cases operators can provide are equally indispensable assets.

Mobile network operators also have a lot to gain. Maybe most importantly, tailored geocoded statistical datasets that can be combined with their own data drastically increase the commercial value of the latter. They also profit from the statistical and domain expertise on offer from statistical institutes, which have been extracting valid and reliable information from raw data for over 200 years. Furthermore, collaborating with official statisticians imparts a quality stamp on the datasets the operators furnish to their commercial clients. And, last but not least, it provides an opportunity to bolster their reputation as a socially responsible corporation perceived to contribute freely to the public good.

Summing up, this suggests a business model in which mobile network operators and statistical institutes both invest in a cooperation to better achieve their respective and non-competing commercial and statistical objectives. Ideally this public-private partnership should lead to a long-term arrangement allowing a statistical institute to produce statistics based, or partly based, on mobile phone data in a sustainable way, while the mobile network operator providing the data obtains additional datasets and statistical support enhancing their data commercialisation business.

Conclusions Several models by which mobile phone data could be used to produce official statistics are possible: network operators could be legally compelled to provide all data or the results of database queries; statistical institutes could buy them; an intermediary integrator could collect the data from the operators, process them and transmit them to statistical institutes or directly to the users of statistics. Each of these models has major flaws making them in practice unfeasible or undesirable or both.

The partnership business model pioneered by Statistics Belgium, Proximus and the European Commission

14 The OECD Statistics Newsletter - Issue No. 65, November 2016

provides incentives to both statistical institutes and mobile network operators to pool resources and data in an equitable way, producing valuable results for all partners, while legal, operational and business issues seem manageable. The collaboration, furthermore, has the potential to evolve towards a long-term relationship essential for the production of statistical time series.

1. 40.3% market share in 2012 (http://economie.fgov.be/nl/consument/Internet/ telecommunicatie/teledistributie).

2. https://ec.europa.eu/eurostat/cros/system/files/assessing_the_quality_of_mobile_ phone_data_as_a_source_of_statistics_q2016.pdf

GOOD STATISTICAL PRACTICE TOOLKIT

2860

3604

Quality statistics are critical for good analysis, transparency, accountability and ultimately the functioning of democracies. The Recommendation of the OECD Council on Good Statistical Practice is a key reference for assessing national statistical systems. The Recommendation applies to OECD members but non-members are also welcome to adhere to it.

This online Toolkit includes: •• The Recommendation along with a set of good practices based on existing international and national guidelines; •• A self-assessment tool based on the list of good practices; •• Completed country assessments; and •• Links to other international guidelines for official statistics.

RECOMMENDATION OF THE OECD COUNCIL ON GOOD STATISTICAL PRACTICE

www.oecd.org/statistics/ good-practice-toolkit stat.recommendation@oecd.org

Estimating the Transport and Insurance Costs of International Trade Guannan Miao (guannan.miao@oecd.org) and Fabienne Fortanier (fabienne.fortanier@oecd.org), Statistics Directorate, OECD

A

lthough the costs associated with the international transport and insurance of merchandise trade (Cost, Insurance and Freight (CIF) – Free on Board (FOB) margins) are an important determinant of the volume and geography of international trade, remarkably little (official) data exist. Combining the largest and most detailed cross-country sample of official national statistics on explicit CIF-FOB margins to date with estimates from an econometric gravity model, and using a novel approach to pool product codes across Harmonized System (HS) vintages, the OECD has developed a new Database on International Transport and Insurance Costs (ITIC) (http://stats.oecd.org/Index. aspx?DataSetCode=CIF_FOB_ITIC) that aims to fill this gap.

The database details the bilateral international trade and insurance costs for more than 180 countries, over 1000 individual products, covering the period

1995-2014, and provides an important new tool to further the understanding of global value chains. This article illustrates how the database provides potential new insights on the role distance, natural barriers such as mountain ranges, and infrastructure, play in shaping regional (and global) value chains. Box 1 briefly summarises the methodology.

It is important to note however, that the new OECD ITIC dataset also has an important direct and related application in the context of the OECD-WTO Trade in Value Added (TiVA) initiative. The global (inter-country) input-output table that underpins TiVA contains, by design, a balanced view of international trade . However, while merchandise export figures are typically reported on a FOB basis (Free on Board), merchandise import figures are usually reported with international transport and insurance costs included (referred to as CIF prices, Cost, Insurance and Freight). The new OECD estimates

Box 1. Methodology for estimating International Trade and Insurance Costs In the absence of detailed data on transport and insurance costs for international merchandise trade, existing research has necessarily used analytical approaches to produce estimates. Typically, either information from one or a few countries is generalised to cover all global merchandise trade flows (The United States are often used as they have very detailed data available), or bilateral mirror data from UN COMTRADE is used (which is less precise, and assumes no other cause for asymmetry, but has the advantage of covering more countries).

The new OECD ITIC database partly follows in these footsteps. However, one of the main improvements is that the underlying econometric model is based on the largest and most detailed cross-country sample of official national statistics on explicit CIF-FOB margins to date used in these kinds of analyses; covering 16 countries (reflecting nearly 20% of global imports) that currently publish or have published detailed bilateral product-level information on CIFFOB margins. This sample will be extended if and when more countries develop similar data; which this initiative hopes to provide momentum to.

The gravity-type model that was developed takes into account the effects of distance, geographical situation (contiguous partners, partners on the same continent), infrastructure quality, oil prices, product unit values and time.

A variety of robustness tests were conducted to test (and confirm) the validity of the results, such as the use of alternative measures for infrastructure quality and splitting the sample into primary and other products. The model was further tested across a broader set of countries, using implicit unit value CIF-FOB margins derived from the UN COMTRADE database for data where bilateral quantities were similar; in other words where the CIF-FOB margin is likely to explain (most or all) of the underlying asymmetry between reporters.

Issue No. 65, November 2016 - The OECD Statistics Newsletter  15

for ITIC are therefore used to remove these costs (the CIF-FOB margin) from the reported import data.

from Vietnam and Hong Kong than from other Asian countries, (reflecting in large part proximity but also the mix of products imported), and Brazil and South Africa.

Similarly, US imports from Mexico and Findings Oil prices (fuel) Canada have much lower CIF-FOB represent an With an estimated trade-weighted margins than those from other trading average (for all countries over the important component partners, as do French imports from period 1995 to 2014) of 6%, the of overall transport European partners. new OECD Dataset on International costs; data confirm the The resulting CIF-FOB margins also Transport and Insurance Costs on anticipated positive highlight how natural geographical merchandise trade (ITIC) shows that effect of crude oil the costs of transport and insurance barriers, such as the Andes mountain are significant, even without taking range in Latin America, and poor intraprices on CIF-FOB account of the multiplicative nature regional infrastructure, such as in Africa, margins (...) of these costs due to the international may impose barriers to the development fragmentation of production that characterises global of regional production chains (although again, other value chains. Notwithstanding the role of other factors factors, including ‘behind-the-border’ constraints, will such as relative costs of production, government policy, also play a significant role). The data in Figure 1, which and just-in-time production methods, geographical show relatively high margins for German imports from distance between trading partners, and the associated Italy (separated by the Alps), further illustrate these point. transportation costs, helps to explain why global German imports from Italy have a CIF-FOB margin equal value chains still retain strong regional dimensions, to that of imports from the US for example. Similarly, as witnessed in ‘Factory Asia’, and the production the Chilean imports do not only have relatively high hubs in Europe and in NAFTA. The ITIC data show CIF-FOB margins, but imports from Brazil, Colombia or for example that inter-continental trade increases Argentina require higher transport and insurance costs transport and insurance costs by between 2 to 4% than those from Germany. as compared to comparable intra-continental trade.

Oil prices (fuel) represent an important component of Figure 1 further illustrates this point for imports of overall transport costs, and the data (and the model) electrical machinery and parts there of (chapter 85 also confirm the anticipated positive effect of crude oil in the 2007 HS classification). It shows that CIF-FOB prices on CIF-FOB margins, as shown in Figure 2. For margins are significantly lower for Chinese imports example, the model shows that globally, a rise in oil prices Figure 1. Trade weighted average CIF-FOB margins on imports of Electrical machinery (model-based estimates 2014*) CIF-FOB margin on imports from selected economies 8%

6%

4%

2%

Source: OECD (2016), International Transport and Insurance Costs (ITIC) - http://stats.oecd.org/Index.aspx?DataSetCode=CIF_FOB_ITIC

16 The OECD Statistics Newsletter - Issue No. 65, November 2016

COL

ARG

KOR

JPN

USA

CHN

BRA

AUT

Importing country: USA Importing country: France Importing country: Germany

DEU

FRA

POL

CZE

HUN

ITA

USA

BEL

CHN

JPN

ESP

USA

ITA

DEU

THA

CHN

CAN

KOR

JPN

MYS

MEX

ZAF

Importing country: China

CHN

BRA

HKG

VNM

JPN

KOR

TWN

0%

Importing country: Chile

Figure 2. Trade weighted average CIF-FOB margins on imports of Electrical machinery (model-based estimates 2014*) percentage point change compared to oil at 25 USD/Barrel

Marginal effect on CIF-FOB margins 2.5 2.0 1.5 1.0 0.5 0.0 -0.5

0

10

20

30

40

50

60

70

80

90

100

Crude oil price per barrel

-1.0 -1.5 -2.0 -2.5

Source: OECD (2016), International Transport and Insurance Costs (ITIC) - http://stats.oecd.org/Index.aspx?DataSetCode=CIF_FOB_ITIC

from 25 to 75 USD per barrel increases the estimated CIF-FOB margin by 1.4 percentage point (all other variables remaining constant). Similarly, a reduction in oil prices from, for example 100 USD per barrel to 50 USD (which is approximately what happened in 2015, when oil prices dropped from 93 to 48 USD per barrel), reduces the CIF-FOB margin by close to 1 percentage point.

Typically, the longer the chain the higher the cumulative costs of transport and insurance. Consider for example an intermediate good of value 100 USD exported from country A to country B, with 6% transportation costs (i.e. an import value of 106 in country B), that is subsequently processed and exported to Country C for 156 USD (with an additional 4% of transportation costs), where the product is finalised and exported to country D for 237 USD (with an 8% transportation cost). The import price in country D will be 256 USD, consisting of a cumulative total of 31 USD in transport and insurance costs (or 12% of the final product value), as illustrated in Figure 3.

International fragmentation of production means that product parts and components cross borders many times during the course of (global) production, each time accumulating international transport and insurance costs.

Figure 3. Fragmented production multiplies transportation costs: example

Country A Primary production

+6%

Country B Intermediate production

100 USD

+4%

+50 USD

Country C

+8%

Final production +75 USD

Country D Final Consumption 256 USD

Imports CIF

Value Added

Exports FOB

ITIC

Country A

-

100

100

6%

Country B

106

50

156

4%

Country C

162

75

237

8%

Country D

256

-

-

-

For further information on the methodology and underlying source data used, see the forthcoming OECD Statistics Working Paper (Miao and Fortanier, 2016, Estimating the Transport and Insurance Costs of International Trade) which will be available at www.oecd.org/std/publicationsdocuments/workingpapers.

Issue No. 65, November 2016 - The OECD Statistics Newsletter  17

Measuring Support to Statistics in Developing Countries Johannes Jütting (johannes.jutting@oecd.org) and Thilo Klein (thilo.klein@oecd.org), PARIS21

I

t is now widely accepted that statistics play a 18 Millennium Development Goal (MDG) targets to 169 fundamental role in development. Several reports on SDG targets is very naïve and misleading. They reckon the 2030 Agenda issued by the United Nations (UN), that producing data to measure the SDG indicators only the ‘A World That Counts’ report by the Independent requires USD 1 billion per year; equivalent to an increase Expert Advisory Group on a Data Revolution for in support to statistics from the current 0.25% of ODA Sustainable Development (IEAG, 2014) and the ‘Road to 0.5%, as measured by the 2016 PARIS21 Partner Map for a Country-Led Data Revolution’ (PARIS21, Report on Support to Statistics (PRESS).

2015) all highlight the need for sound statistics to inform policies and to measure progress. Nevertheless, the The Partner Report on Support to Statistics Partnership in Statistics for Development in the 21st (PRESS) Century (PARIS21) finds that the increased focus on statistics for development is still not well reflected in To obtain clarity on current funding levels, the annual official aid figures (PARIS21, 2016). In 2014, official PRESS report provides a snapshot of ongoing worldwide support to statistics remained relatively low, comprising a financial support to statistical development including mere 0.25% of Official Development Assistance (ODA). an overview of trends. This information is intended to This has wide-ranging consequences, as without timely inform discussions on current investments and facilitate and accurate statistics, countries will not be able to collaboration among partners for the co-ordination underpin effective and sustained of resources. The PRESS report policies, programmes and services to measures financial support provided In 2014, official help lift the poor out of poverty – and support to statistics by multilateral and bilateral donors keep them there. covering all areas of statistics ranging remained relatively from national accounts to human Given the relevance of this topic, it is low, comprising a mere resources and training. PARIS21 crucially important to have an accurate designs and administers the PRESS 0.25% of ODA. picture of both current funding levels exercise to inform discussions on as well as the expected funding requirements. Many statistics, offering insights into when and where support countries still struggle to meet statistical demands, is being channelled. The 2016 PRESS report covers particularly at a time of shrinking government budgets, results for calendar years 2006 to 2014 (see Figure 1). To despite the increased awareness of the importance ensure comparability over time, the same methodology of statistics. More efforts are therefore needed to is applied retrospectively for all years. ensure that developing countries have the capacity and resources to better monitor progress on their own policy The annual commitments in Figure 1 are subject to objectives as well as the newly adopted Sustainable fluctuations. This is because the reported commitments, Development Goals (SDGs). The UN’s push for “data for worth several millions of US Dollars, often cover several development” is a very helpful step in this direction, but years. So caution is warranted when interpreting annual scepticism in the development community remains: The movements as trends. The bottom line, however, is that Economist (2015), in its January 24th issue, quotes the support to statistics remains low in both absolute and Copenhagen Consensus Centre’s Bjorn Lomborg stating relative terms. that at USD 17 billion per year, “gathering data is hugely expensive […] compared to other ‘value for money’ PRESS Methodology investments for the SDGs”, such as “providing modern cooking fuels to 780m people”, for which they estimate an The methodology of this report has been developed to economic return of USD 15 per dollar spent. A coalition address several methodological challenges. Today, the of development experts (Espey et al., 2015), however, PRESS gives the most comprehensive account of how argue that Lomborg’s extrapolation of the cost of data much the international community spends on statistics, collection (USD 1.5 billion per target for 15 years) from in which areas and for which regions and countries.

18 The OECD Statistics Newsletter - Issue No. 65, November 2016

Figure 1. Global commitments to statistics, million USD CRS, Sector code

PRESS Questionnaire

CRS, other sectors

Total

$578m

$600m

$95m

$500m

$577m $524m $56m

$408m $400m $324m $27m

$300m

$125m

$65m

$410m

$42m

$333m

$123m

$93m

$366m

$162m

$371m

$347m

$217m $200m

$41m

$188m

$470m

$347m $219m

$136m

$74m $100m

$203m $102m

$109m

$104m

2006

2007

2008

$68m 2009

Data sources The aim of the PRESS is to provide a full picture of international support to statistics. The main source of information is the OECD Creditor Reporting System (CRS), which records data from members of the OECD Development Assistance Committee (DAC) and some non-DAC donors with the goal to provide a comprehensive accounting of ODA. Donors report specific codes for the sector targeted by their aid activity.

Statistical capacity building (SCB) is designated by code 16062. However, when SCB is a component of a larger project, it is not identified by this code, causing the CRS figures to underestimate actual levels of support of international aid for statistics. PARIS21 seeks to reduce this downward bias by searching project descriptions in the CRS for terms indicating a component of SCB. Finally, the PARIS21 Secretariat supplements this data with an online questionnaire completed by a global network of reporters. The questionnaire covers Box 1: PRESS 2016 Highlights Total financial support to statistics

$470 M

Share of Official Development Assistance dedicated to statistics

0.25%

Demographic & social statistics received the most support

$141 M

Percent of commitments from top 5 providers

72%

2010

$117m

$97m

$105m

2011

2012

2013

$58m 2014

a subset of the variables collected in the CRS and some additional variables specific to statistical capacity building. Reporting to the questionnaire is voluntary, offering an opportunity for actors to share information on their statistical activities. Reporters to this questionnaire include countries that do not report to the CRS as well as multilateral institutions with large portfolios of statistical projects that have been requested to report to the PARIS21 Secretariat directly.

Methodological challenges and solutions Measuring support to statistics comes with many methodological challenges, meaning that the financial figures presented in the PRESS need to be interpreted with some care. For instance, despite the efforts described above, full coverage of all programmes cannot be guaranteed. However, to arrive at estimates that are as robust as possible, the PRESS methodology has developed solutions to address the most important reporting challenges: 1. Double counting of projects may occur when the donor and project implementer report on the same project or when all project co-financers report project totals.

To circumvent this problem, multilateral reporters to the PRESS questionnaire indicate their role as “implementer” (vs. “donor”) when they manage or implement a project financed by another donor.

Such reporting allows the PARIS21 Secretariat to ensure that these commitments appear only once in the global number, resulting in a more accurate estimate.

Issue No. 65, November 2016 - The OECD Statistics Newsletter 19

2. A limitation of the CRS is that it does not allow reporters to enter a list of recipients when reporting on multi-recipient projects. It is thus unclear which share of the support goes to a specific country.

The issue of allocating the funding to the appropriate recipients is of particular importance for reporting the PRESS headline number for the indicator under SDG Target 17.19: “Financial and other resources made available to strengthen the statistical capacity in developing countries” at the country level. To solve this identification problem, PARIS21 recommends that reporters split their projects into sub-projects – one per recipient country – with each carrying their respective share of the total project commitment.

Goal 17. As this year’s PRESS reflects data for 2014, i.e. around the end of the MDGs, an increase in funding is expected in the next few years with the adoption of the SDGs, which should be reflected in the PRESS exercise of 2018.

A continuing trend is that new actors are starting to support statistical development financially. One such case is the Gates Foundation’s commitment of USD 80 million for gender data. It is therefore to be expected that there will be a change in the top contributors in the coming years with private foundations playing a larger role. Many of these new players set themselves very ambitious and quantifiable objectives, with the consequence that development projects will become increasingly data and results-driven.

3. The reported numbers may be inflated by working with project totals for multi-sector projects, which comprise only a small statistics component.

PARIS21 recommends that reporters split their projects into sub-projects – one per sector.

This reporting practice is followed to indicate what share of a multi-sector aid project goes to statistics. Whenever possible, PARIS21 encourages this practice and has incorporated it into its methodology to ensure the successful measurement of resources available to strengthen statistical capacity at the country level.

Way forward The 2016 PRESS report shows that aid to statistics remains stagnant at around 0.25% of ODA. To increase this figure, it is important to break the vicious cycle of low investment in statistics that is often a result of low quality statistics and consequently little use and demand for the data. To that end, PARIS21 works with national statistical systems on different entry points, with the aim of improving the data availability, advancing the understanding of statistics among policy makers, and convincingly demonstrating the impact that statistics has on peoples’ lives. PARIS21 also undertakes research to compare situations where statistics are available to similar contexts where they are not. This counterfactual analysis allows to measure the impact for society as well as the economic return on investment.

There is also a growing awareness of the importance of statistics by the international community, which is for example reflected in the formulation of the SDGs. The PRESS headline number on the annual support to statistics is now monitored as an indicator for SDG

20 The OECD Statistics Newsletter - Issue No. 65, November 2016

References The Economist (2015). The economics of optimism: The debate heats up about what goals the world should set itself for 2030, www.economist.com/ news/finance-and-economics/21640361-debateheats-up-about-what-goals-world-should-setitself-2030 Espey et al. (2015). Data for Development: A Needs Assessment for NSDS Monitoring. United Nations Sustainable Development Solutions Network, http:// unsdsn.org/wp-content/uploads/2015/04/Data-forDevelopment-Full-Report.pdf OECD (2007). Reporting Directives for the Creditor Reporting System, www.oecd.org/dac/ stats/1948102.pdf PARIS21 (2015). A Road Map for a Country-Led Data Revolution. PARIS21, http://datarevolution. paris21.org PARIS21 (2016). Partner Report on Support to Statistics. PARIS21, http://paris21.org/PRESS2016 IEAG (2014). A World That Counts: Mobilising the Data Revolution for sustainable development, www.undatarevolution.org

Forthcoming meetings Unless otherwise indicated attendance at OECD meetings and working parties is by invitation only.

OECD

Date

Meeting

5-6 December

2nd meeting of the OECD Expert Group on Extended Supply Use Tables, Statistics Directorate.

OECD, Paris, France Task Force on Pension Statistics and Working Party on Private Pensions, Directorate for Financial and Enterprise Affairs. OECD, Paris, France Task Force on Insurance Statistics, Directorate for Financial and Enterprise Affairs. OECD, Paris, France Subjective well-being over the life course: Evidence and policy implications, Statistics Directorate. London, United Kingdom 2nd Meeting of the Advisory Group on the Measurement of Trust, Statistics Directorate. OECD, Paris, France 2nd Meeting of the Advisory Group on Measuring the Quality of the Working Environment, Statistics Directorate. OECD, Paris, France Measuring business impacts on people’s well-being, Statistics Directorate. OECD, Paris, France Joint OECD/International Transport Forum Statistics meeting. OECD, Paris, France OECD Space Forum Workshop on Innovation Indicators for the Space Sector, Directorate for Science, Technology and Innovation. OECD, Paris, France Working Group on International Investment Statistics, Directorate for Financial and Enterprise Affairs. OECD, Paris, France Working Party on International Trade in Goods and Services Statistics (WPTGS), Statistics Directorate. OECD, Paris, France Working Party on Indicators of Educational Systems (INES), Directorate for Education and Skills. Ljubljana, Slovenia Meeting of the Working Party of National Experts on Science and Technology Indicators (NESTI), Directorate for Science, Technology and Innovation. OECD, Paris, France 3rd meeting of the OECD Expert Group on Extended Supply Use Tables PARIS21 Annual meeting, PARIS21. OECD, Paris, France G20 Thematic Meeting on Institutional Sector Accounts, Statistics Directorate. OECD, Paris, France Working Party No. 2 on Tax Policy Analysis and Tax Statistics, Centre for Tax Policy and Administration. OECD, Paris, France Eurostat-OECD-UNECE Group of Experts on National Accounts: Measuring Global Production, Statistics Directorate. Geneva, Switzerland OECD Week, OECD, Paris, France Working Party on Tourism Statistics, 1st Session, Centre for Entrepreneurship, SMEs and Local Development. OECD, Paris, France UNECE Conference of European Statisticians, UNECE. Geneva, Switzerland OECD Committee on Statistics and Statistical Policy, Statistics Directorate. Geneva, Switzerland

5-6 December 7 December 2016 12-13 December 2016 14-15 December 2016 12-13 January 2017 23-24 February 2017 14-15 March 2017 15-20 March 2017 20-23 March 2017 20-24 March 2017 21-23 March 2017 27-31 March 2017 28-29 March 2017 5-7 April 2017 10-12 April 2017 16-18 May 2017 31 May-2 June 2017 6-9 June 2017 13 June 2017 19-21 June 2017 21-22 June 2017

Other meetings 30 Nov.-2 Dec. 2017 12 December 2016 17-20 January 2017 21-23 April 2017

Women’s Forum for the Economy and Society, Deauville, France. www.womens-forum.com/meetings/global-meeting-2016 New World Forum 2016, Paris, France. www.forum-nouveaumonde.org/en World Economic Forum Annual Meeting, Davos-Klosters, Switzerland. www.weforum.org/events/world-economic-forum-annual-meeting-2017 2017 Spring Meetings of the World Bank Group and the International Monetary Fund Washington, D.C.

Issue No. 65, November 2016 - The OECD Statistics Newsletter 21

Recent publications World Energy Outlook 2016 The landmark Paris Agreement on climate change will transform the global energy system for decades to come.

The latest World Energy Outlook offers the most comprehensive analysis of what this transformation of the energy sector might look like, thanks to its energy projections to 2040. It reviews the key opportunities and challenges ahead for renewable energy, the central pillar of the low-carbon energy transition, as well as the critical role for energy efficiency.

WEO-2016 examines how a post-Paris world redefines the idea of energy security, particularly in the power sector, the frontline in the fight against climate change. The report explores how oil, natural gas and coal are adjusting to today’s market conditions and assesses the risks that lie ahead, from under-investment in essential supply to stranded assets.

IEA (2016), World Energy Outlook 2016, IEA, Paris. www.iea.org/newsroom/news/2016/november/world-energy-outlook-2016.html Latin American Economic Outlook 2017 The 2017 edition of the Latin American Economic Outlook explores youth, skills and entrepreneurship.

Young Latin Americans embody the region’s promise and perils. They stand at the crossroads of a region whose once promising economy and social progress are now undergoing a slowdown.

The Outlook identifies potential strategies and policy responses to help Latin America and the Caribbean revive economic growth. While development can stem from different sources, skills and entrepreneurship can empower youth to develop knowledge-intensive economic activities, boost productivity and transform the region’s politics as they transition successfully from the world of school to the world of productive work and create that future they seek. The report highlights valuable experiences and best practices in these fields and proposes strategies to allow Latin America to consolidate long-term growth while assuring continuity in the social agenda.

OECD/CAF/ECLAC (2016), Latin American Economic Outlook 2017: Youth, Skills and Entrepreneurship, OECD Publishing, Paris. www.latameconomy.org/EconomicOutlook/ OECD Regional Outlook 2016: Productive Regions for Inclusive Societies Regions and cities are where the effects of policies to promote economic growth and social inclusion are felt in day-to-day life. The OECD Regional Outlook 2016 examines the widening productivity gap across regions within countries, and the implications of these trends for the wellbeing of people living in different places. It discusses how structural policies, public investment and multi-level governance reforms can help boost productivity and address inclusion. Drawing on a survey of OECD countries, the Outlook highlights country practices in regional, urban, and rural development policy that guide public investment. The Special Focus Part II on rural areas looks at different types of rural area and their productivity performance trends, and suggests that countries move towards a “Rural Policy 3.0”.

OECD (2016), OECD Regional Outlook 2016: Productive Regions for Inclusive Societies, OECD Publishing, Paris. www.oecd.org/gov/oecd-regional-outlook-2016-9789264260245-en.htm

22 The OECD Statistics Newsletter - Issue No. 65, November 2016

Recent publications OECD Society at a Glance 2016 Young people who leave school at 16 with low skills are facing increasing challenges in finding a job, and their chances may not improve even if the economy picks up, according to Society at a Glance 2016.

Society at a Glance 2016 says that about 40 million young people in OECD countries, equivalent to 15 per cent of youth aged 15 to 29, are not in education, employment or training, so-called NEETs. Two-thirds of them are not even looking for work. While up to 40% of all youth experience a period of inactivity or unemployment over a four year period, for half of them this period will last a year or more and may lead to discouragement and exclusion.

OECD (2016), Society at a Glance 2016: OECD Social Indicators, OECD Publishing, Paris. www.oecd.org/social/society-at-a-glance-19991290.htm

International Migration Outlook 2016 OECD countries need to address the growing anti-immigration backlash and reinforce migration and integration policies while fostering international cooperation in this area, according to the OECD International Migration Outlook 2016.

The share of the public holding anti-immigration views has grown, driven by concerns that borders are insecure, immigrants stretch local services and some do not want to integrate. The 2016 International Migration Outlook stresses that systematic and co-ordinated action is needed to vigorously address these concerns and tap into the many opportunities that migration offers to recipient economies and societie.

OECD (2016), International Migration Outlook 2016, OECD Publishing, Paris. www.oecd.org/migration/international-migration-outlook-1999124x.htm

Education at a Glance 2016 Education at a Glance is the authoritative source for information on the state of education around the world. It provides key information on the output of educational institutions; the impact of learning across countries; the financial and human resources invested in education; access, participation and progression in education; and the learning environment and organisation of schools.

The 2016 edition introduces a new indicator on the completion rate of tertiary students and another one on school leaders. It provides more trend data and analysis on diverse topics, such as: teachers’ salaries; graduation rates; expenditure on education; enrolment rates; young adults who are neither employed nor in education or training; class size; and teaching hours. The publication examines gender imbalance in education and the profile of students who attend, and graduate from, vocational education.

OECD (2016), Education at a Glance 2016: OECD Indicators, OECD Publishing, Paris. www.oecd.org/edu/education-at-a-glance-19991487.htm

Issue No. 65, November 2016 - The OECD Statistics Newsletter  23

The Statistics Newsletter

for the extended OECD statistical network Issue 65 - November 2016 www.oecd.org/std/statisticsnewsletter @OECD_STAT