The OECD Statistics Newsletter, Issue 67, November 2017 by The OECD Statistics and Data Directorate

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

FEATURING ++How's Life? 2017 ++Development Co-operation Report 2017 ++EIGE's third edition of the Gender Equality Index

THE LATEST UNDERSTANDING FINANCIAL ACCOUNTS OECD TiVA NOWCAST ESTIMATES www.oecd.org/std/statisticsnewsletter Issue No. 67, November 2017

Contents 3

How’s Life? 2017 Carrie Exton, OECD Statistics Directorate (carrie.exton@oecd.org)

Development Co-operation Report 2017: bringing data to the centre of development policy Johannes Jütting, PARIS21, OECD Statistics Directorate (johannes.jutting@oecd.org) and Ida McDonnell, OECD Development Co-operation Directorate (ida.mcdonnel@oecd.org)

Highlights from EIGE's third edition of the Gender Equality Index European Institute for Gender Equality (EIGE) (eige.sec@eige.europa.eu)

How Good is Your Job? Fabrice Murtin, OECD Statistics Directorate (fabrice.murtin@oecd.org)

The European Commission releases the first ever edition of the Cultural and Creative Cities Monitor to support urban policies and investments Valentina Montalto, Carlos Jorge Tacao Moura, Sven Langedijk, Michaela Saisana, European Commission, Joint Research Centre - Directorate for Competences, Modelling, Indicators and Impact Evaluation Unit (jrc-coin@ec.europa.eu)

Forthcoming meetings

Recent publications

The Statistics Newsletter is published by the OECD Statistics Directorate. This issue and previous issues can be downloaded from oe.cd/statisticsnewsletter To receive the OECD Statistics Newsletter by email, you can subscribe to OECDdirect e-mails: www. oecd.org/about/publishing/oecddirect.htm Follow us on

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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: 15 May 2018

The OECD Statistics Newsletter - Issue No. 67, November 2017

How’s Life? 2017 Carrie Exton, OECD Statistics Directorate (carrie.exton@oecd.org)

hat makes for a good life has been life overall. This recognises that how people feel is debated for millennia. While that an important component of their overall well-being – debate is and if we want to describe The OECD well-being framework far from whether life is getting better b e i n g for people, their own views CURRENT WELL-BEING resolved, anyone seeking about their lives should also to improve people’s lives be part of the picture. Most through public policy needs of the data are sourced from access to good information National Statistical Offices about whether, where and (NSOs), including making for whom life is getting better. use of established OECD Now in its fourth edition, databases. However, in a How’s Life? is a statistical small number of cases where report that provides an there is a lack of harmonised RESOURCES FOR FUTURE WELL-BEING international perspective international data, indicators Sustaining well-being over time through preserving: on well-being in OECD and from non-official data sources partner countries. It aims are used as placeholders to offer insights into what is (e.g. for social support, and happening to people’s lives at feelings of safety). the individual, household and community level, to complement the picture that policy Life is better for some – but some elements makers gain from looking at aggregate measures of the of well-being are getting left behind economic system as a whole. The last ten years have posed many challenges for The OECD well-being framework, developed in 2011, people’s well-being. The financial crisis had a deep and considers both the outcomes that matter to people’s long-lasting impact on several aspects of people’s lives, current well-being (divided, broadly, into material and particularly on their jobs. How’s Life? 2017 considers conditions and quality of life) as well as the resources changes in well-being over time, with a particular focus that help to sustain well-being over time (natural, on the simple question: is life now better or worse than it was in 2005, before the crisis took hold? While some human, economic and social capital). Describing how things have improved for the average OECD resident, outcomes are distributed among the population is a key progress has often been slow, and several aspects of feature of the measurement approach, and the 2017 well-being are getting left behind. edition of How’s Life? includes a chapter devoted to well-being inequalities, as well as a chapter looking at the experiences of the migrant population in OECD The picture is equally mixed for the resources that countries. sustain well-being over time. Here again, OECD average progress since 2015 in terms of some critical resources The indicators featured in How’s Life? mostly focus on (e.g. falling per capita greenhouse gas emissions, a objective data about people’s living conditions – from reduction in smoking prevalence, greater investment in household income and wealth, through to adult skills, R&D, and an increase in the stock of produced economic exposure to air pollution, life expectancy, homicide assets per capita) is offset by declines for other indicators rates, and voter turnout. Nevertheless, there are also in many countries (e.g. rising household debt, falling some indicators that reflect how people think about financial net worth of government, growing obesity, and and experience their lives – including their health, falling trust in government). social support, feelings of safety, and satisfaction with

Issue No. 67, November 2017 - The OECD Statistics Newsletter

How's Life? 2017

Cumulative gains and falls in OECD average well-being since 2005

MEASURING WELL-BEING

BETTER THAN 2005

WORSE THAN 2005

ANNUAL EARNINGS

LABOUR MARKET INSECURITY

in 28 OECD countries 7% cumulative increase, in real terms, for the OECD on average

LIFE EXPECTANCY AT BIRTH in 27 OECD countries

in 27 OECD countries 33% increase for the OECD on average

VOTER TURNOUT

in 19 OECD countries 2.4 percentage point fall for the OECD on average

1.7 year increase for the OECD on average

HOUSEHOLD INCOME in 22 OECD countries 8% cumulative increase, in real terms, for the OECD on average

FEELING SAFE WALKING ALONE AT NIGHT in 20 OECD countries 3 percentage point increase for the OECD on average

LONG-TERM UNEMPLOYMENT in 17 OECD countries 0.3 percentage point increase for the OECD on average

SOCIAL SUPPORT in 9 OECD countries 3 percentage point fall for the OECD on average

average, it has gone up by at least that much in 40% of all OECD countries. Conversely, for indicators where the OECD average has worsened, there are usually several countries bucking the trend with a more positive story. For example, long-term unemployment has worsened in half of all OECD countries since 2005, but improved in a quarter of them. How’s Life? 2017 presents well-being profiles for 35 OECD countries and 6 OECD partner countries – showing how different their experiences are in terms of both current well-being and resources for the future. This provides countries with an overview of comparative strengths and weaknesses, while also detailing information indicatorby-indicator on whether the situation is worsening or improving over time. Inequalities in well-being: Who is getting left behind? Income inequalities have taken centre-stage in recent years, but the differences in people’s well-being within OECD countries go well beyond income. The findings from How’s Life? 2017 suggest that while some societies are more equal than others, there are pockets of both high and low inequality in all OECD countries. For example, while some Nordic countries (e.g. Finland, Norway, Sweden and Denmark) fare comparatively well on the gender divide, and have some of the smallest gaps between high- and low-achieving groups overall, differences in well-being between the young and middleaged adults can be large, relative to the OECD average. Comparative strengths and weaknesses in current well-being (excerpt from a country profile) SUBJECTIVE WELL-BEING

EMPLOYMENT

LIFE EVALUATION

in 18 OECD countries

in 8 OECD countries

1.3 percentage points increase for the OECD on average

0.2 scale points fall for the OECD on average

http://bit.ly/how-is-life #howslife

Not all OECD countries are alike

What is true for the OECD on average is rarely true for all OECD countries – and this is particularly the case when it comes to well-being gains and losses since 2005. Life expectancy is the only headline indicator that has improved since 2005 in all OECD countries where it can be assessed. In other cases, even when the OECD average has improved, there are pockets of worsening performance. For example, while the share of household income spent on rents and mortgages has fallen by half a percentage point for the OECD on

The OECD Statistics Newsletter - Issue No. 67, November 2017

PERSONAL SECURITY Feeling safe at night

INCOME AND WEALTH Life satisfaction

Household income

Household net wealth Employment

Homicides*

ENVIRONMENTAL QUALITY

Earnings

Air quality

Labour market insecurity*

Water quality

JOBS AND EARNINGS

Job strain* Voter turnout

CIVIC ENGAGEMENT AND GOVERNANCE

Long-term unemployment*

Having a say in government Rooms per person Social support

SOCIAL CONNECTIONS

Housing affordability

Cognitive skills at 15

Basic sanitation

Adult skills

EDUCATION AND SKILLS

Educational attainment

HOUSING

Working hours Perceived Life health expectancy

Time off

WORK-LIFE BALANCE

HEALTH STATUS

Note: In each country profile, this chart shows relative strengths and weaknesses in well-being when compared with other OECD countries. For both positive and negative indicators (such as homicides, marked with an “*”), longer bars always indicate better outcomes (i.e. higher wellbeing), whereas shorter bars always indicate worse outcomes (i.e. lower well-being). Source: OECD (2017), How's Life? 2017: Measuring Well-being

Inequalities also interact – and these relationships are degree are overqualified for their jobs, compared to important to assess, since a “winner takes all” society one-fifth of the native-born population. (one where the same people are at the top of the ladder in all aspects of life) is inherently more unequal than A gap between public institutions and the one where, for example, the people who have the best people they serve health are different from those who have the best jobs. One important relationship is that between income and The steady decline in voter turnout among OECD wealth. A high share of people in OECD countries lack the countries — a sign of increasing civic disengagement wealth buffer needed to protect themselves from income — has been a cause for concern for many years. How’s shocks. If they had to forgo 3 months of their income, Life? 2017 discusses other ways in which people feel close to 40% of people would lack the distant from the public institutions Migrants are more financial wealth needed to provide a that serve them. More than half of likely to work antiliving standard above the poverty line, OECD residents consider corruption based on evidence from 25 OECD social hours, to be in to be widespread in their government. countries. The share of people who are low-paid jobs and to Trust in public institutions has fallen economically vulnerable is lowest (at over the last 12 years, and only 33% be exposed to risky around 1 in 5) in Austria and Norway, of people feel they have a say in what or harmful working and highest (at more than one in 2) the government does. conditions. in the Slovak Republic, Greece and Latvia. But interrelationships among People with fewer resources (in terms inequalities do not end there. People in the top 20% of education, income and jobs) are less likely to vote, and income bracket are twice as likely as those in the bottom less likely to feel able to influence policy decisions. For 20% to report a high life satisfaction. And people with example, self-reported voter turnout is 14 percentage a high life satisfaction (above 8, on a 0 to 10 scale) are points lower for people in the bottom income quintile, 4 times more likely to report being in good health when compared to those in the top quintile. Moreover, people compared to those with low life satisfaction (below 3 without an upper secondary qualification are 40% less on the same scale). likely to feel they have a say in what the government does, relative to those with a tertiary education.

Minorities and well-being: How’s life for migrants? Migrants make up a small but significant minority in most OECD countries: on average, 13% of the OECD population was born abroad. How’s Life? 2017 describes the challenges of measuring migrants’ well-being, as well as the evidence that is currently available. For example, although employment rates among migrants’ are similar to those for the native-born, the employment gap between men and women is larger among migrants. Moreover, the quality of jobs available to migrants, is markedly worse: they are more likely to work anti-social hours, to be in low-paid jobs (with 17% experiencing in-work poverty, compared to 8% of the native-born), and to be exposed to risky or harmful working conditions. The median income of migrants is 25% lower, and median average net wealth is around half that of the native-born. In many cases, migrants find that they are unable to make the most of the skills that they bring with them. While having a degree boosts migrants’ chances of finding work, it does not provide them with the same job opportunities as the native-born: the employment rate of highly-educated migrants is 9 points lower than that of the highly-educated native-born, on average. In addition, almost one-third of migrants with a tertiary

In terms of satisfaction with democracy, Europeans are generally satisfied with how elections are run, but much less so with policy actions to reduce inequalities. Satisfaction with public education and health services varies widely across countries, but tends to be higher among people who have used these services recently. This suggests that experience matters when it comes to shaping people’s perceptions of how public institutions work. Want to know more? Visit w w w.oecd.org/howslife to see the full report, key findings, country snapshots, and a dataviz on inequalities. OECD (2017), How's Life? 2017: Measuring Wellbeing, OECD Publishing, Paris

Issue No. 67, November 2017 - The OECD Statistics Newsletter 5

Development Co-operation Report 2017 Bringing data to the centre of development policy Johannes Jütting, PARIS21, OECD Statistics Directorate (johannes.jutting@oecd.org) and Ida McDonnell, OECD Development Co-operation Directorate (ida.mcdonnel@oecd.org)

s c ountr ies and hand. The DCR also features profiles “The continued lack development actors and performance of development of basic data along ensure that no one is co-operation assistance providers with weak statistical and presents statistics on official and left behind, this call for better statistics has systems remain major private resource flows. This 55th edition become a principal issue on the of the DCR provides a comprehensive stumbling blocks to 2030 Agenda. Data and statistics achieving the SDGs”, analysis on the data constraints faced are an essential part of the OECD’s by developing countries today, and OECD Secretarywork to inform policy decisions. In the the policy options to compile better General, Angel Gurría statistics for sustainable development. development field, with the adoption of the Sustainable Development Goals (SDGs), data evidence is increasingly important for designing policies There is a worrying data divide… and strategies to achieve the goals. Still, the continued lack of basic data and weak statistical systems remain a A global data divide persists today in developing challenge in the developing world. Only about one-third countries. This divide is characterised, on the one hand, of the 232 global SDG indicators adopted by the UN have by the scarcity of basic data about people and the planet, been rated as “Tier I”, meaning that the methodology is and the weak incentives and capacity to fill these gaps. agreed and the data are available. At the other end of the scale, the roughly one-third of indicators rated “Tier …but unprecedented opportunities come III” do not even have agreed methods, let alone data. with the data revolution And even for many of the Tier I indicators, much work On the other hand, there is a surge in new data sources will still be required to improve the granularity of the and types of data enabled by digital technologies. The data – disaggregating it by various population groups impact of this new data ecosystem, and big data in so as to ensure that the SDGs “leave no one behind”. particular, on the global economy has become a highly topical subject of research and debate. The data To bring the development and improvement of statistics revolution has the potential to transform the operations to the forefront of the development agenda, the OECD of national statistical systems in rich and poor countries has dedicated its 2017 Development Co-operation alike. Developing countries have been embarking on it for Report (DCR) to “Data for Development”. Each year, quite some time now. Social media, records from the use the DCR addresses an important challenge for the of mobile telephones, sensors, web scraping and satellite international development community and provides imagery represent some of the new information sources practical guidance and recommendations on the issue at offering an opportunity for better data. In Bangladesh,

Good data for development are lacking 44% of countries worldwide do not have comprehensive birth and death registration data

13% of countries worldwide have a dedicated budget for gender statistics

Source: OECD (2017), Development Co-operation Report 2017: Data for Development,

6 The OECD Statistics Newsletter - Issue No. 67, November 2017

37 countries have statistical laws that meet UN standards

No data exist for two thirds of Sustainable Development Goals indicators

Haiti, Kenya, Nigeria and Tanzania, governments are using geospatial information to understand socioeconomic phenomena including educational outcomes (e.g. literacy, numeracy) and access to contraceptives. Geospatial data can also help monitor socioeconomic and environmental conditions and enable geographic disaggregation. However, to fully capitalise on these data sources, a number of conditions need to be fulfilled, including universal access to the internet. For example, more than 3.9 billion people are still offline (OECD/ WTO, 2017). National statistical systems at the centre of the data ecosystem In the context of the data revolution, national statistical systems will have to operate increasingly as part of a larger community of data producers and users, including civil society, the private sector, academic and scientific communities. National statistical offices (NSOs) will have to play a critical co-ordination role in this new environment. They will need to enter in partnerships with new data actors. Redesigning national statistical systems and investing more resources in building the necessary statistical capacity should become a strategic priority for developing countries and for development co-operation providers. The ecosystem of data production and use OFFICIAL STATISTICS DATA COMMUNITIES

NATIONAL STATISTICAL OFFICES

PRIVATE SECTOR & BIG DATA COMMUNITIES

SCIENTIFIC DATA COMMUNITIES

Data production

Planning & production Dissemination Literacy Use & value

SDG DATA MONITORING & USE

PUBLIC POLICY FORMULATION

THE GLOBAL CHALLENGE

WHAT'S NEW

Better policies demand better data

To achieve the Sustainable Development Goals, we need to know more about people's lives

New technology makes it easier, faster and cheaper to produce better data for policy making

Good data for development leadership in developing countriesaretolacking ensure that the abundance of data actually enables development. This 44% of 37 13% of No data exist countries countries countries for two thirds worldwide involves promoting the case of data for development, worldwide while ensuring that statistics are produced according to high-quality standards, also protecting privacy and confidentiality. do not have comprehensive birth and death registration data

have a dedicated budget for gender statistics

have statistical laws that meet UN standards

of Sustainable Development Goals indicators

Together, development partners can help bridge the data dividethe data divide Six concrete data actions to bridge

SIX DATA ACTIONS Make statistical laws, regulations and standards fit for evolving data needs

Increase efficiency and impact of investment in data and capacity building through co-ordinated, country-led approaches

Improve the quantity and quality of financing for data

Invest in and use country-led results data to monitor progress made towards the Sustainable Development Goals

Boost data literacy and modernise statistical capacity building

Make data on development finance more comprehensive and transparent

Source: OECD (2017), Development Co-operation Report 2017: Data for Development Source: OECD (2017), Development Co-operation Report 2017: Data for Development, OECD Publishing, Paris, http://dx.doi.org/10.1787/dcr-2017-en

Data action 1. Make statistical laws, regulations and standards fit for evolving data needs. To build inclusive data ecosystems, institutional and legal frameworks need to be fit for purpose. The growing number of actors and institutions involved in data production stress the need for clear legal and quality standards. Immediate actions in this direction are proposed: developing and updating statistical and open statistical laws, authorising NSOs to adopt new modes of data collection, new forms of partnership and data dissemination. The recent experience of OECD countries can be helpful here. Data action 2. Improve the quantity and quality of financing for data. Investing in statistical systems must be a priority for developing countries and their development co-operation partners alike. For this, financing has to increase and be stable, if national statistical systems are to respond to the growing data demand. Innovative mechanisms for domestic resource mobilisation for statistics, public-private partnerships and data philanthropy should be developed.

CIVIL SOCIETY & CITIZEN-BASED DATA COMMUNITIES

Investment

ACADEMIC RESEARCH & BUSINESS DECISIONS

WHAT WE KNOW

Data use & impact

Source: OECD (2017), Development Co-operation Report 2017: Data for Development, based on GPSDD (2016), “The state of development data funding 2016”, http://opendatawatch.com/ wp-content/uploads/2016/09/development-data-funding-2016.pdf

Concrete actions to bridge the data divide The DCR report identifies six key data actions to make the most of the power of data for sustainable development. Each one will require strong political

Data action 3. Boost statistical capacity and data literacy through new approaches. New, more comprehensive approaches to statistical capacity development need to be developed and piloted. These should go beyond building capacity to collect data Taking into account three distinct features – people, organisations and the enabling environment – a new capacity development 4.0 should become best practice for NSOs. This will entail putting emphasis in soft skills, including leadership, change management, strategic thinking and advocacy, and taking into account the users’ perspective.

Issue No. 67, November 2017 - The OECD Statistics Newsletter 7

Data action 4. Increase efficiency and impact through “data compacts” or other co-ordinated, country-led approaches. Developing countries should better align incentives for producing data for national policy making and global monitoring through mutually accountable inclusive partnerships among data producers and users. The establishment of data compacts for co-ordinating and harmonising investment in data and support for statistical systems is a promising approach. For this, aligning incentives for producing data for national strategies will be essential. Data action 5. Invest in and use country-led results data to monitor progress towards the SDGs. External actors need to support country-led strategies and data ecosystems. This requires clear vision and pragmatism in dealing with results-based frameworks. It also means ensuring that results from independent data collection efforts are made accessible to development actors to avoid unnecessary duplication. Data action 6. Produce and use better data to help understand the overall state of SDG financing. Better data on development finance are needed so as to get a more comprehensive financing picture and allow developing countries to better plan and budget their national development strategies. Improving statistics for sustainable development is a task for all. Developing countries cannot do this job alone. They need the political and financial support to develop the statistics and the analytical tools that will show how agreed national and global objectives can be met. To start, finance and planning ministries must guarantee adequate funding over the medium-term to develop sound national statistical systems and institutions. At the same time, development co-operation providers need to provide efficient and consistent financial support to guarantee long-lasting improvements in the provision and quality of statistics. Above all, developing countries’ needs for comprehensive, timely and predictable data should drive and shape this agenda. References

OECD (2017), Development Co-operation Report 2017: Data for Development, OECD Publishing, Paris, www.oecd.org/dac/ development-co-operation-report-20747721.htm OECD/WTO (2017), Aid for Trade at a Glance 2017: Promoting Trade, Inclusiveness and Connectivity for Sustainable Development, World Trade Organization, Geneva/OECD Publishing, Paris, http://dx.doi. org/10.1787/aid_glance-2017-en PARIS21 (2017), National Strategy for the Development of Statistics Guidelines, OECD, Paris, http://nsdsguidelines.paris21.org

8 The OECD Statistics Newsletter - Issue No. 67, November 2017

Entrepreneurship at a Glance The 2017 edition of Entrepreneurship at a Glance (oe.cd/Entr-at-a-glance), released in September 2017, presents key indicators for measuring the state of entrepreneurship and the SME sector. Data explored in the publication confirm an upturn in the entrepreneurial environment, with positive trends in the creation of new enterprises, driven by the services sector, and a generalised decline in bankruptcies. The 2017 edition also shows that productivity gaps between large enterprises and SMEs are much smaller in the services sector than they are in manufacturing; and that post-crisis labour productivity growth in SMEs in the manufacturing sector lagged that of large enterprises, exacerbating existing productivity gaps between small and large firms.

Source: OECD (2017), Entrepreneurship at a Glance 2017., OECD Publishing, Paris. http://dx.doi.org/10.1787/ entrepreneur_aag-2017-en

The report continues to expand the illustration of gender differences in entrepreneurship through new indicators, and also introduces emerging topics, in particular the development of the “gig economy”, as reflected by selfemployment data, and its relation with entrepreneurship; and the use of digital tools by SMEs to participate in international trade.

Understanding Financial Accounts The OECD has recently released Understanding Financial Accounts (http://oe.cd/UFA). This new publication is a fully co-operative effort between the OECD, the BIS, the ECB, Fondazione AIB, the IMF, national central banks (Austria, Italy and Portugal), national statistical offices (Australia and Canada), and the Treasury of Canada. The publication seeks to show how a range of questions on financial developments can be answered with the framework of financial accounts and balance sheets, by providing non-technical explanations illustrated with practical examples: What are the basic principles, concepts and definitions used for this framework which is part of the system of national accounts? What sources and which methodologies are used for their compilation? How are these used to monitor and analyse economic and financial developments? What can we learn about the 2007-2009 economic and financial crisis when looking at the numbers provided in this framework? What can we learn about financial risks and vulnerabilities? This publication is intended for young statisticians, students, journalists, economists, policy makers and citizens, who want to know more about the statistics that are at the heart of the analysis of financial developments in OECD economies. The publication can be ordered at http://dx.doi.org/10.1787/9789264281288-en

Job vacancy - Senior Statistician or Analyst The OECD Statistics Directorate is looking for a Senior Statistician or Analyst to lead the statistical and conceptual work of the Sectoral and National Accounts Section of the National Accounts Division. S/He will oversee the Sectionâ&#x20AC;&#x2122;s work programme, manage research projects on conceptual and measurement issues, ensure statistical quality, and contribute to the development of new statistical systems and communication tools in the areas of the Section. If you have proven experience in the area of macroeconomic statistics, of which several spent in a national or international statistical agency, we are interested in hearing from you! All qualified applicants are encouraged to apply. Review the full job description here https://oecd.taleo.net/careersection/ext/jobdetail. ftl?lang=en&job=11818, and apply online before midnight 07/01/2018 (Paris time), and feel free to share this opportunity with your network!

Issue No. 67, November 2017 - The OECD Statistics Newsletterâ&#x20AC;&#x192; 9

Highlights from EIGE's third edition of the Gender Equality Index 1

European Institute for Gender Equality (EIGE) (eige.sec@eige.europa.eu)

Measuring gender gaps over time he Gender Equality Index is a composite indicator, which aims at synthesising the complex and multidimensional concept of gender equality into a single measure; it captures the different outcomes of the European Union (EU) and national policies for women and men and supports the development and implementation of evidence-based policy making in the area of gender equality. The Gender Equality Index measures inequalities between women and men across six core domains (work, money, knowledge, time, power, and health), and takes account of different levels of achievement in the EU Member States. The index is presented on a scale from 1 to 100, with a higher score reflecting low gender gaps and a high level of achievement for both women and men. Based on a conceptual framework that embraces EU policy priorities and different theoretical approaches to gender equality, the Gender Equality Index includes 31 indicators that are aggregated according to the principles set out in the OECD/European Commission’s Structure of the Gender Equality Index

Joint Research Centre handbook on the development of composite indicators.2 Innovations for a comprehensive picture of gender equality The third edition of the Gender Equality Index includes a number of new developments.3 The Index now shows trends in gender equality over the past ten years, covering four points in time – 2005, 2010, 2012 and 2015 – for all 28 EU Member States. The Index also provides more granular insights by highlighting how gender inequalities vary depending on a person’s age, education, family composition, country of birth and disability.4 The Gender Equality Index now also includes data on health and decision-making, providing measures of gender gaps in decision-making in research, media and sports and data on women and men’s health and risk behaviour.5 As a complement to the Index, the third edition also provides a composite measure on the extent of violence against women; which takes account of prevalence and severity of violence but also on how any women have disclosed their experiences of violence. Although the composite measure is not included in the overall Gender Equality Index score, it helps raise awareness of violence against women in the EU6 and serves as an important tool to monitor Member States’ efforts to combat it, such as support services, policy and legal frameworks.7 Some progress, but at a snail’s pace The Index shows that progress towards gender equality in the EU-28 has been slow, with the overall score increasing by only four points in the past 10 years, from 62 in 2005 to 66.2 in 2015. However, levels of inequality and indeed the pace of change vary significantly across countries. Over the last decade, nearly all EU Member States have become more

The OECD Statistics Newsletter - Issue No. 67, November 2017

Gender Equality Index, scores for the EU Member States, 2005 and 2015 100 90 80 70

66.2

Scores

62.0

50 40 30 20 10 01

2015

gender-equal, with the exception of the Czech Republic, Slovakia and the UK. Significant improvement occurred in Italy (+12.9 points). Three Nordic Member States (Sweden, Denmark and Finland) and the Netherlands were the most gender-equal Member States in 2015. Indeed, Sweden and Denmark have been the most gender-equal societies throughout the 10-year period (2005-2015). Substantial progress in decision-making, but it still remains the most gender-unbalanced area Looking at specific domains within the broader composite measure, the biggest improvement occurred in measures of power.8 Progress in gender-balanced decision-making was most pronounced in corporate boards, where the proportion of women at the EU level more than doubled from 10% in 2005 to 22% in 2015. This progress largely reflects legislative action by governments to improve gender balance. Despite these advances however, ‘power’ is the domain with the highest levels of inequality. Decision-making in the economic and financial realm remains the most unbalanced area, especially in central banks, which are still dominated by men.9 Setback on gender equality in time use Gender inequalities in time spent on housework and caring for dependent family members (children, the elderly and people with disabilities) and social activities (leisure activities and volunteering) are persistent and growing. At 65.7 points, this domain has the third lowest score in the Gender Equality Index. Only 34% of men engage in cooking and housework daily for one hour or more on average in the EU, compared to 79% of women. The care burden is especially high among women living in the EU but born outside the EU. Furthermore, the unequal division of time spent on caring impacts on the time available for social activities.

2005

Men have more time for sporting, cultural or leisure activities. The consequences of such inequalities are farreaching. Unequal division of time-use, particularly when it comes to care and household responsibilities, is likely to be a factor in gender pay and pension gaps, women’s economic independence, equality in employment and career progression. Inequalities are larger for some groups The Index also highlights some of the differences, across the key domains,10 among groups of women and men. In the domain of money, for example, people born outside the EU are twice as likely to be at risk of poverty than people born in the EU. When it comes to the knowledge domain, inequalities are particularly stark for educational attainment levels reached by young men.11 In the health domain, the scale of access to health and dental services is strongly related to family type, with particular difficulties encountered by single mothers. These examples, and others included in the Index, show the various ways in which age, education, and country of birth, disability and family composition impact on inequalities across a range of different domains. As such, the Gender Equality Index is an important tool to support Member States in identifying challenges and developing policy responses that take account of the various needs of women and, indeed, men who are at a disadvantage. In addition, and as highlighted in previous reports of the EIGE, addressing gender inequalities in the EU can help boost GDP and productivity.12 See full report at: http://eige.europa.eu/rdc/eigepublications/gender-equality-index-2017-measuringgender-equality-european-union-2005-2015-report All data are downloadable from the Gender Equality Index web interface: http://eige.europa.eu/gender-statistics/ gender-equality-index

Issue No. 67, November 2017 - The OECD Statistics Newsletter 11

Notes: 1. The data in this article and in Figure 2 do not reflect the OECD's position on Cyprus and are the sole responsibility of the authors. As a reminder, the OECD position on Cyprus is:

The Pursuit of Gender Equality

Note by Turkey: The information in this document with reference to “Cyprus” relates to the southern part of the Island. There is no single authority representing both Turkish and Greek Cypriot people on the Island. Turkey recognises the Turkish Republic of Northern Cyprus (TRNC). Until a lasting and equitable solution is found within the context of the United Nations, Turkey shall preserve its position concerning the “Cyprus issue”.

In October 2017, the OECD released The Pursuit of Gender Equality. An Uphill Battle (oe.cd/gender2017), a publication prepared in the framework of the OECD Gender Equality Initiative. The report analyses developments in education, employment and entrepreneurship, and reviews socio-demographic changes and the state of violence against women. Drawing on new data, the report highlights that gender gaps persist in all areas of social and economic life and across countries, and points to actions to close gender gaps worldwide.

Note by all the European Union Member States of the OECD and the European Union: The Republic of Cyprus is recognised by all members of the United Nations with the exception of Turkey. The information in this document relates to the area under the effective control of the Government of the Republic of Cyprus. 2. OECD and European Commission Joint Research Centre, 2008, Handbook on Constructing Composite Indicators. Available at: www.oecd.org/ std/42495745.pdf 3. Details on new developments as well as updates to the methodology are described in European Institute for Gender Equality (EIGE) (2017) Gender Equality Index 2017: Methodological report. 4. Intersectional analysis is applied to the whole index at the variable level – variables are disaggregated by gender and one more discussion at a time (e.g. age and gender; age and education etc.), as much as data availability allows. As a result, this allows for an analysis of the levels/situations of different sub-groups separately as well as gender gaps within sub-populations. Furthermore, this approach points out data gaps and the need to improve collections of harmonised and segregated data. 5. The sub-domain of health behaviour includes factors, based on WHO recommendations, on healthy behaviour, namely fruit and vegetable consumption, and physical activity. It also includes data on health risk behaviour, namely, smoking and alcohol consumption. 6. The full theoretical and measurement framework of the domain of violence, including the rationale behind the choice of variables, steps taken to compute the composite measure on violence against women, and data analysis for all indicators is described in EIGE’s publication Gender Equality Index 2017: Measurement framework of violence against women released in November 2017 (http://eige.europa.eu/rdc/eige-publications/gender-equality-index2017-measurement-framework-of-violence-against-women). 7. The domain of violence includes three layers: (1) a set of indicators on the extent of violence against women that will form the composite measure; (2) a set of additional indicators covering a broader range of forms of violence against women; (3) a set of contextual factors that include some of the root causes of violence against women and information on governments’ efforts to combat violence against women. 8. The domain of power includes three sub-domains: political decision-making (ministries, parliaments and regional assemblies/local councils), economic (boards of largest quoted companies and national central banks) and social power (boards of public broadcasters, largest sports federations and research funding organisations). 9. Women’s share on boards of national central banks has increased only by a fraction, from 16% in 2005 to 19% in 2015. 10. Except for the domain of power, due to the lack of data. 11. While in older generations more men than women have achieved tertiary education, the gender gap is reversed among younger people (aged 25-49). The proportion of women aged 30-34 participating in tertiary education was 43% in 2015, compared to 34% of men in the same age group. 12. European Institute for Gender Equality (EIGE) (2017a), Economic benefits of gender equality in the European Union, Briefing Paper, Publications Office of the European Union, Luxembourg, available at: http://eige.europa.eu/ sites/default/files/documents/economic_benefits_of_gender_equality_briefing_paper.pdf

12 The OECD Statistics Newsletter - Issue No. 67, November 2017

For the report, the Gender Initiative conducted a stocktaking on countries’ progress in implementing policy measures aimed at reaching gender equality goals, including the promotion of gender statistics. The 2013 OECD Gender Recommendation (www.oecd. org/gender/C-MIN(2013)5-ENG.pdf) invited member and non-member countries to “ensure the collection, production and development of timely and internationally comparable gendersensitive data and indicators” and to “ensure that adequate resources are allocated to this important activity”. Since 2013, many countries have engaged in developing their gender statistics, although levels of ambition and activities differ. In countries where gender statistics are well developed, for instance Australia, Canada and Nord European countries, most of the initiatives that have been taken concern efforts to promote dissemination of the indicators produced or to actions related to raising awareness throughout the production process. In a few countries, such as Colombia, development has been fast and thorough, as the entire statistical process has been gender mainstreamed across all areas, with a major impact on the entire statistical production process.

How Good is Your Job? Fabrice Murtin, OECD Statistics Directorate (fabrice.murtin@oecd.org)

he case for looking at the quality of the working environment is quite straightforward. Indeed, work is of fundamental importance for our well-being. Policy demand for data in this area is high and set to increase in the near future. One of the goals of the 2030 Agenda agreed by the UN General Assembly in September 2015 is to “Promote inclusive and sustainable economic growth, employment and decent work for all” (Goal 8), with more specific targets to “achieve full and productive employment and decent work for all”, “protect labour rights and promote safe and secure working environments for all workers”, and “eradicate forced labour, end modern slavery and human trafficking and secure the prohibition and elimination of the worst forms of child labour”. While a first set of indicators for the global monitoring of Goal 8 has already been identified by the statistical community, this set is likely to evolve in the future to better match changing labour-market realities. Reflecting this evolution, the OECD is also currently revisiting its “Job Strategy” – the blueprint that has underpinned the labour-market reforms implemented by many of its member countries since the 1980s and 90s.The new jobs strategy is giving an important role to the concept of Job Quality, which includes the quality of the working environment as one of its three dimensions, alongside earnings quality and labour market security (Cazes, Hijzen and Saint Martin, 2015). Labour market conditions are thus no longer assessed in terms of quantity of jobs only but also in terms of their quality, and of whether jobs provide the basis for a dignified existence for workers and their families. As compared to other aspects of the “Beyond GDP” agenda, a substantial body of evidence and statistical practice already exists in the field of the working environment, largely reflecting long-established regulations to address health and safety concerns in the workplace. But much of the available evidence is based on non-comparable country surveys, with comparative evidence largely limited to European countries. Also, the nature of the working environment has evolved over time, reaching beyond the physical risk-factors that were the focus of traditional health and safety regulations. Comparable evidence on the broader range of socioenvironmental aspects that shape working conditions

remains limited, despite evidence of their importance for both workers’ well-being and firms’ productivity. The consequences of a poor working environment include burnout, disengagement, absences from work and mental health problems among workers. The OECD Guidelines for Measuring the Quality of the Working Environment (oe.cd/measuring-job-quality), which were released on 23 November 2017, aim to support statistical offices and other data producers in their efforts to measure the quality of the working environment through surveys of people with paid jobs. They take stock of the measurement initiatives undertaken in this field by UNECE, ILO and the EU in the past, propose a general conceptual framework to operationalise this concept, discuss a range of methodological issues, and propose three survey modules that could be included by national statistical offices (NSOs) in their various surveys vehicles. A new measurement framework In these Guidelines, as well as in the broader OECD framework on job quality (www.oecd.org/statistics/jobquality.htm), the ‘working environment’ is understood as a combination of job characteristics defining the setting where workers operate. The concept is multidimensional and encompasses a broad range of non-pecuniary characteristics of jobs ranging from the nature of the work tasks assigned to each worker to the physical and social conditions under which these tasks are carried out, the characteristics of the firm or organisation where work takes place, the scheduling of working time, the prospects that the job provides to workers and the intrinsic rewards associated with the job. The concept denotes those observable characteristics of the job as they are experienced by workers. The OECD Guidelines recommend that job characteristics are measured by looking at outcomes rather than procedures (e.g. labour codes or firm-level practices); that they refer to experiences of individual workers rather than what is observed at the aggregate level; and that they capture objective aspects of the job rather than purely subjective evaluations of it. An impressive body of research, reviewed in the Guidelines, has demonstrated the relevance of the

Issue No. 67, November 2017 - The OECD Statistics Newsletter 13

•• Job prospects, which are linked to job insecurity (i.e. quality of the working environment for workers’ well-being job demands), training and learning opportunities as and health conditions. In particular, the job demandsresources model (Demerouti, et al., 2001)1 stresses well as opportunities for career advancement (i.e. the importance of balancing the demands of the job job resources). and the resources that are available to workers to meet those demands. •• Finally, the intrinsic aspects of the 17 key job This model underpins the OECD Job job, which refer to opportunities for characteristics, Quality Framework and constitutes viewed either as a self-realisation and intrinsic rewards the background after which the OECD (i.e. job resources). job resource or a job Guidelines have been developed.2 demand, classified Taken together, these six dimensions Previous research and consultations with allow a comprehensive assessment of into six broad an Expert Group set up by the OECD to dimensions have the working environment, suitable for support the production of the Guidelines comparing countries, sectors and firms. been set up. have led to the identification of 17 key job characteristics, viewed either as a job resource or a job Building a questionnaire to measure the demand, classified into six broad dimensions (see Table): quality of the working environment •• The physical and social environments of work, which include physical risk factors and physical demands (i.e. job demands) and social support at work (i.e. job resources). •• Job tasks, which capture work intensity and emotional demands (i.e. job demands) and autonomy or task discretion (i.e. job resources). •• Organisational characteristics, which cover organisational participation and workplace voice, good managerial practices, task clarity and performance feedback (i.e. job resources). •• Working-time arrangements, related to unsocial work schedules (i.e. job demands) and to the flexibility of working hours (i.e. job resources).

The Guidelines indicate how this measurement framework can be operationalised through survey questions. This is done by assessing the statistical validity of data sourced from various surveys, with broad country coverage that provide information on various job characteristics. For each characteristic, the Guidelines review questions from existing international and national surveys, and examine the extent to which questions from different surveys produce consistent results across countries. Building on this assessment, the Guidelines propose three prototype question modules on the working environment, which could be implemented in different survey vehicles depending on space constraints. These prototype modules range from an extended module of 25 questions covering all 17 job characteristics, which could be included as a stand-alone module or survey,

Table: Measurement framework for the quality of the working environment Dimensions

Job characteristics Job demands

A. Physical and social environment

Job resources

A.1. Physical risk factors A.2. Physical demands

A.4. Social support at work

A.3. Intimidation and discrimination at the workplace B. Job tasks

B.1. Work intensity B.2. Emotional demands

C.1. Organisation participation and workplace voice C.2. Good managerial practices C.3. Task clarity and performance feedback

C. Organisational characteristics D. Worktime arrangements E. Job prospects

B.3. Task discretion and autonomy

D.1. Unsocial work schedule E.1. Perceptions of job insecurity

F. Intrinsic aspects

14 The OECD Statistics Newsletter - Issue No. 67, November 2017

D.2. Flexibility of working hours E.2. Training and learning opportunities E.3. Opportunity for career advancement F.1. Opportunities for self-realisation F.2. Intrinsic rewards

Box: A core module of items to measure the quality of the working environment FROM NOW ONWARDS ALL THE QUESTIONS REFER TO THE MAIN PAID JOB “To what extent do you agree or disagree with the following statements about your main job?” All statements should be answered with the following response scales: Scale A (1) Completely disagree (2) Disagree (3) Neither disagree nor agree (4) Agree (5) Completely agree

Scale B (1) Never (2) Rarely (3) Sometimes (4) Often (5) Always

(6) Not applicable (7) Don’t know (8) Refused to answer Job demands: 1. “My job involves working at very high speed or to tight deadlines” 2. “I am expecting to lose my job in the next 6 months”

(Scale B) (Scale A)

Job resources: 3. “I am able to choose or change my methods of work” 4. “I learn new things in my job”

(Scale A) (Scale A)

to a condensed module of the 13 questions with the strongest evidence on statistical validity, and limited to the 11 job characteristics that are most relevant for workers’ well-being, to a core module of just 4 questions, that could be included in non-specialised general social surveys and implemented on a yearly basis (see Box). The implementation of the Guidelines by statistical offices and other data producers would go a long way towards meeting the policy demand for broader and more comparable evidence on the many job aspects that shape workers well-being and firms' productivity. They could also feed into the construction of a more robust and comprehensive Job Strain Index,3 of the type that is already calculated by the OECD (based on non-official data and limited to only a few of the job characteristics that are conceptually relevant) to measure the prevalence of jobs in which workers face an imbalance between demands and resources. The methodology underpinning the OECD Job Strain Index is currently being revisited, in light of changes introduced in the available international surveys. Further work in this field will include the development of a more comprehensive index including all of the job demands and job resources described in the OECD Guidelines.

To learn more, consult the OECD Guidelines on Measuring the Quality of the Work Environment: http:// oe.cd/measuring-job-quality

1. Demerouti, E. et al. (2001), “The job demands-resources model of burnout”, Journal of Applied Psychology,Vol. 86, pp. 499-512. 2. The OECD Job Quality framework is developed in Cazes, S., A. Hijzen and A. Saint-Martin (2015), “Measuring and assessing job quality: The OECD Job Quality Framework”, OECD Social, Employment and MigrationWorking Papers, No. 174, OECD Publishing, Paris. http://dx.doi.org/10.1787/5jrp02kjw1mr-en. 3. The Job Strain Index is a composite index showing the share of workers experiencing more job demands than the number of job resources available to them. The OECD Job Strain Index is composed of various sub-indicators, data on which are also available in the OECD database: these refer to the share of workers experiencing time pressures or facing physical health risk factors (two job demands), and having learning and training opportunities or social support at work (two job resources). These individual-level measures are aggregated to identify the share of workers with high job demands or low job resources. The index is disaggregated by gender, education and age (https://stats.oecd. org/Index.aspx?DataSetCode=JOBQ).

Issue No. 67, November 2017 - The OECD Statistics Newsletter 15

Measuring Global Value Chains newly released OECD TiVA Nowcast estimates The OECD-WTO Trade in Value Added (TiVA) indicators have provided a new prism through which international trade and the global fragmentation of production can be viewed. However, the underlying national supply-use (SUT) and input-output (IOT) tables required to produce TiVA estimates are typically not available until at best two to three years after the reference periods to which they refer, and, in addition, involve a lengthy process of integration within a coherent global accounting framework. To begin to tackle this issue, the OECD has developed a procedure and method, which capitalises on more timely national accounts and trade data, to nowcast national SUT and IOT and, in turn, the underlying inter-country input-output tables used to generate TiVA estimates. The methodology, which is described in more detail online,1 is characterised by three main elements. Firstly, through the use of volume and current price estimates and associated price indices, it explicitly accounts for (differential) price movements. Secondly, it converts 2008 SNA data into 1993 SNA equivalents for goods for processing and merchanting transactions, in order to better reflect the policy drivers for TiVA data, which essentially require a view of physical production processes (including for example when goods cross borders and have tariffs imposed); as opposed to the ‘financial’ process that underpins the 2008 SNA. Thirdly, the method is iterative in nature, in order to provide a degree of stability in the evolution of technology/production coefficients and consumption patterns over time. The dataset with Nowcast TiVA indicators up to 2014 can be accessed and downloaded from OECD.Stat.2 As an example of the insights that can be derived from these data, the figure below shows that growth in international fragmentation (measured as the foreign content share of exports) stalled in the 2012-2014 period, albeit at record highs. This global trend is generally replicated at the national level, with foreign content shares of exports stabilising across all major economies in recent years, including for example in China, where the trend for rising domestic content seen in the preceding period (in part reflecting upgrading) has also stabilised in the most recent years. Global trade in gross and value-added terms, USD billions Exported Value-Added

Gross exports

Foreign content of exports (RHS)

25000

29% 27%

20000 25% 15000

23% 21%

10000

19% 5000 17% 0

1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

15%

Source: OECD (2017), "Trade in value added, nowcast estimates", OECD-WTO: Statistics on Trade in Value Added (database).

The development of nowcast estimates of TiVA is a work-in-progress and a number of improvements, conditional on the availability of new data sources and improvements in existing data sources, are currently in development. Significant efforts will also continue on the development of additional national data sources and their integration in the nowcast system. Chief in this respect are the national supply-use tables, whose official collection using a standard reporting template has recently begun within the OECD, where TiVA has also helped to motivate the development of more timely statistics.3 1. www.oecd.org/std/its/tiva-nowcast-methodology.pdf 2. https://stats.oecd.org/Index.aspx?DataSetCode=TIVA_NOWCAST 3. www.oecd.org/std/na/supply-and-use-tables-database.htm

16 The OECD Statistics Newsletter - Issue No. 67, November 2017

his year, the European Union (EU) celebrates the tenth anniversary since the adoption of the first ‘European Agenda for Culture in a Globalising World’ (2007). Since then, the cultural and creative sectors have taken an increasingly prominent place in EU policy making as a key driver of growth and job creation, enhancing creativity and innovation as well as fostering a sense of belonging and cohesion among citizens. However, mapping cultural and creative assets and measuring their impact in a systematic and comparable way across Europe remains a challenge, with no shared definitions or metrics, particularly at city level. The Cultural and Creative Cities Monitor aims at providing a reliable comparable evidence base designed to help national, regional and municipal policy makers identify local strengths and opportunities, to promote mutual exchange and learning between cities and to inspire fit-for-purpose policies to boost economic development and strengthen resilience. The first edition of the Monitor, which was officially launched by the European Commission on 6 July 2017, shows how well 168 selected cities in 30 European countries (EU28, plus Norway and Switzerland) perform on 29 carefully selected indicators grouped along nine dimensions. These indicators measure the ‘Cultural Vibrancy’ of a city in terms of cultural infrastructure and participation of the local population and visitors in cultural activities; its ‘Creative Economy’, as regards the extent to which the cultural and creative sectors contribute to job creation and innovation locally; and its ‘Enabling Environment’ capturing a range of ‘enablers’ (such as human capital, a climate of openness, tolerance and trust, and transport connections) that help cities attract creative talent and stimulate cultural engagement. Key

qualitative facts (e.g. main cultural sites, art schools or live events, funds, creative incubators) complement what is measured by the set off indicators above, proving the city’s commitment to support culture and creativity-led development. Cities were selected on the basis of their demonstrable engagement in the promotion of culture and creativity. This has been operationalised using the three following criteria: 1. 93 cities which have been or will be European Capitals of Culture up to 2019, or which have been The Cultural and Creative Cities’ Monitor conceptual framework

Note: the dimension ‘Creative & Knowledge-based Jobs’ has been assigned to ‘Creative Economy’ because it is more statistically related to this facet of a Cultural and Creative City. However, from a conceptual point of view, it relates to ‘Cultural Vibrancy’ as well because it captures the presence of cultural professionals, amongst other creative jobs. This is why the section on ‘Creative & Knowledge-based Jobs’ in the figure is between ‘Cultural Vibrancy’ and ‘Creative Economy’.

Issue No. 67, November 2017 - The OECD Statistics Newsletter 17

shortlisted to become European Capitals of Culture up to 2021; 2. 22 UNESCO Creative Cities (including winners in 2015); 3. 53 cities hosting at least two regular international cultural festivals. The Monitor was developed following the methodology detailed in the JRC-OECD Handbook on Constructing Composite Indicators (2008). Both the data - which combine official statistics and other, experimental ‘big data’ sources (TripAdvisor at present; OpenStreetMap in the next edition) - and the full methodology can be accessed at https://composite-indicators.jrc.ec.europa. eu/cultural-creative-cities-monitor/. Using the Monitor The Cultural and Creative Cities Monitor’s visualisation tool allows users to:

>> browse quantitative and qualitative information

>> explore policy and research questions such as: In

which cities do ‘Cultural Vibrancy’ and ‘Creative Economy’ seem to reinforce each other most? Do culture and creativity contribute to jobs/growth?

The ‘Create your own Monitor’ page – where users can add new city entries but also adapt weights – has been inspired by the OECD Better Life Index’s online platform where the Index’s components can be weighted by users to reflect their personal preferences on the importance of the various aspects of well-being. European cities are already using the Monitor to support their policy actions. The Councilor for the Economy and City Promotion of the Municipality of Bologna, for instance, has invited the Joint Research Centre (JRC) to officially present the Monitor’s findings as a sound evidence base to back its 'IncrediBOL' programme which supports business-oriented projects in the cultural and creative sectors with a view to further strengthen the city’s creative economy. Key findings

to understand strengths and development opportunities for the 168 selected cities;

Cultural and Creative Cities have more jobs and a more diverse work force

>> create a new city entry and compare it to peer

Compared to other European cities with a minimum of 50,000 inhabitants,1 the Cultural and Creative Cities included in the 2017 Cultural and Creative Cities Monitor have, on average:

cities (based on income level, population size or employment rate);

>> adapt the weights of the nine dimensions to reflect local priorities and produce customised rankings;

>> build scenarios by simulating the impact of policy actions (e.g. increased shows and concerts);

18 The OECD Statistics Newsletter - Issue No. 67, November 2017

•• 19% more jobs per capita (or 7.55 percentage points (p.p.) more);

Note: The figures were obtained through econometric analysis, controlling for effects related to: being a capital city or not, city size, unemployment rate, working age population (20-64) in the city, and country and time trends. Although the Cultural and Creative Cities Monitor covers a snapshot of five years (from 2010 to 2015), the estimations refer to different time periods, depending on data availability. Effects on students, highly educated people and foreigners have been computed only for 2011, while effects on young people have been estimated for the period 2010 to 2013, and for workers and jobs for the period 2010 to 2012. However, no causality can be inferred from these results without further analysis.

•• 8% more young people (20-34 years old) per capita (or 1.76 p.p. more); •• 73% more students in higher education per capita (or 4.85 p.p. more) and 15% more highly educated people per capita (or 2.65 p.p. more); •• 22% more EU foreigners (or 0.13 p.p. more) and 26% more non-EU foreigners (or 0.56 p.p. more) per capita. In addition, the Cultural and Creative Cities Monitor shows that the high ranked cities tended to grow faster during the crisis. The ideal Cultural and Creative City in Europe is a mix of eight cities of mostly small and medium size The ‘ideal’ Cultural and Creative City in Europe (shown below) would be a combination of the best performing cities on each dimension. More specifically, it would have

the Cultural Venues & Facilities of Cork (Ireland), the Cultural Participation & Attractiveness and the Creative & Knowledge-based Jobs of Paris (France), the Intellectual Property & Innovation of Eindhoven (The Netherlands), the New Jobs in Creative Sectors of Umeå (Sweden), the Human Capital & Education of Leuven (Belgium), the Openness, Tolerance & Trust of Glasgow (United Kingdom), the Local & International Connections of Utrecht (The Netherlands) and the Quality of Governance of Copenhagen (Denmark). Of these eight cities, five have fewer than 500,000 inhabitants, namely Cork, Eindhoven, Umeå, Leuven and Utrecht.2 Capitals fly high but not the highest Capitals tend to be the top performing cities on ‘Cultural Vibrancy’. However, there are significant exceptions: in fifteen countries, non-capital cities – mostly of medium size – outperform capitals.

Issue No. 67, November 2017 - The OECD Statistics Newsletter 19

‘Cultural Vibrancy’ scores within EU countries Capital city

spreading agglomeration advantages beyond capitals.

Other cities

Cork 65

Florence

Granada

Ghent Bruges

Norwich

Challenges and going forward

Weimar

Tartu

Krakow Sibiu

s'Hertogenbosch

Linz

25 15 5

BG RO PL LT SK ES IT UK HR FI BE DE EE HU EL SI AT SE CZ NL IE DK PT FR

Note: selected EU countries for which data are available.

The Cultural and Creative Cities Monitor represents a first attempt towards better measurement and understanding of how Cultural and Creative Cities of diverse socioeconomic characteristics behave and perform across Europe, based on the most relevant and comparable data available at city level.

The polycentric pattern of ‘Cultural Vibrancy’ may indicate that cities of diverse size can be successful in attracting and retaining educated and creative individuals. According to recent literature3, in a post-industrial economy, highly skilled individuals would indeed seem to prefer amenity-rich locations with plentiful cultural and entertainment opportunities.

However, many other relevant factors matter but these are hard to measure in a comparable way, such as policy frameworks, funds, ICT connectivity, the existence of local/international clusters and networks, the formal and informal ‘creative education’ available, the creative ‘atmosphere’ in a city or the presence of informal types of cultural venues such as cultural clubs or dance schools.

On ‘Creative Economy’, capital cities perform considerably better than non-capital cities. The only exceptions are in Austria, Italy, Germany, Sweden and the Netherlands.

The Monitor is expected to be updated every two years in order to ensure that it remains both conceptually and statistically sound across countries, cities and time, and that progress can be tracked.

This result might be due to the fact that cultural and creative sectors benefit from agglomeration advantages which may be more prominent in capital cities, where more people and economic activities are usually concentrated compared to non-capital cities. However, a significant gap between the capital and the rest of the country’s cities could be a cause for concern, as it may signify a capital city under stress and/or under-exploitation of cultural and creative resources in other cities, calling for

1. Meaning the approximately 800 other European cities with a minimum of 50,000 inhabitants included in Eurostat’s Urban Audit, excluding the 168 selected for the Cultural and Creative Cities Monitor.

2. Due to its population, London is not among the ‘top’ Cultural and Creative Cities because nearly all the Cultural and Creative Cities Monitor indicators are expressed in per capita terms. This approach is primarily intended to enable cross-city comparability but also to give more prominence to more ‘inclusive’ cities which have more cultural and creative assets per inhabitant. As London eclipses other European ‘Creative Economy’ scores within EU countries cities with its population of eight million (almost three Capital city Other cities times as big as the second largest EU city, Berlin), it does not lead on any dimension in the overall ranking, but it does reach seventh place among the 21 cities in its population group. Also, London comes third in Stuttgart the ranking of capital cities, after Paris and Brussels.

75 65 55

Umeå

Linz

Eindhoven

Milan

35 25 15 5 EL

HR AT

EE HU

PT BG UK

Note: selected EU countries or which data are available.

20 The OECD Statistics Newsletter - Issue No. 67, November 2017

RO NL

BE DK

3. See, for instance, Backman, M. and Nilsson, P. (2016). The role of cultural heritage in attracting skilled individuals, Journal of Cultural Economics, 1-28; Nelson, A. C., Dawkins, C. J., Ganning, J. P., Kittrell, K. G., and Ewing, R. (2015). The Association Between Professional Performing Arts and Knowledge Class Growth: Implications for Metropolitan Economic Development, Economic Development Quarterly, 1-11; Clark, T. N., Lloyd, R., Wong, K. K. & Jain, P. (2002). Amenities drive urban growth. Journal of Urban Affairs (24), 493–515.

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

OECD Date

Meeting

5 December 2017

Working Party on Territorial Indicators, Centre for Entrepreneurship, SMEs, Local Development and Tourism. OECD, Paris, France Advisory Expert Group (AEG) on National Accounts, Statistics Directorate. New York, United States

5-7 December 2017 6 December 2017 7 December 2017 7-8 December 2018 14-15 December 2017 15-16 January 2018

21-22 February 2018 9-10 March 2018 19-23 March 2018 19-23 March 2018 26-28 March 2018 11-12 April 2018 16 April 2018 14-17 May 2018 23-25 May 2018 29-31 May 2018 18 June 2018 18-22 June 2018 17-18 September 2018 5-9 November 2018 27-29 November 2018

Advisory Expert Group on Measuring the Impacts of Business on People's Well-being, Statistics Directorate. OECD, Paris, France Worshop Revenue Statistics in Asia and the Pacific Islands, Centre for Tax Policy and Administration. Suva, Fiji 2017 Global Forum on Competition, OECD, Paris, France www.oecd.org/competition/globalforum Working Party of National Experts on Science and Technology Indicators (NESTI), Directorate for Science, Technology and Innovation. OECD, Paris, France International Forum on Migration Statistics, Directorate for Employment, Labour and Social Affairs and Statistics Directorate. OECD, Paris, France www.oecd.org/migration/forum-migration-statistics OECD-UNECE Seminar on the Implementation of the System of Environmental Economic Accounting, Statistics Directorate. Geneva, Switzerland Conference on Research in Income and Wealth, Statistics Directorate. Washington D.C., United States 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 OECD, Paris, France Consolidated European Supply-Use and Input-Output Tables Technical Group Meeting, Statistics Directorate. OECD, Paris, France Working Party on Territorial Indicators, Centre for Entrepreneurship, SMEs, Local Development and Tourism. OECD, Paris, France Working Party No. 2 on Tax Policy Analysis and Tax Statistics, Centre for Tax Policy and Administration. OECD, Paris, France ITF Summit 2018: Transport Safety and Security, International Transport Forum. Leipzig, Germany http://2018.itf-oecd.org OECD Week. OECD, Paris, France Working Party on Tourism Statistics, Global Forum on Tourism Statistics. OECD, Paris, France Committee on Statistics and Statistical Policy (CSSP) - Conference of European Statisticians (CES), Statistics Directorate. Geneva, Switzerland IAOS Conference: Better Statistics for Better Lives, International Association for Official Statistics (IAOS) and Statistics Directorate. OECD, Paris, France www.oecd.org/IAOS2018 Working Party on Financial Statistics and on National Accounts (WPFS/WPNA), Statistics Directorate. OECD, Paris, France 6th OECD World Forum on Statistics, Knowledge and Policy, Statistics Directorate. Incheon, Korea

Other meetings 6-10 December 2017 7-8 December 2017

G200 Youth Forum 2017, Dubai, the United Arab Emirates www.g200youthforum.org/events Forum of the Americas, Paris, France http://forum-americas.org/paris/home

12 December 2017 20-22 April 2018

One Planet Summit, Paris, France www.oneplanetsummit.fr/en 2018 Spring Meetings of the International Monetary Fund and World Bank Group. Washington, D.C., United States UN World Data Forum, Dubai, the United Arab Emirates undataforum.org/WorldDataForum

22-24 October 2018

Issue No. 67, November 2017 - The OECD Statistics Newsletterâ&#x20AC;&#x192; 21

Recent publications Revenue Statistics: 1965-2016 Personal income taxes are playing an increasingly significant role in the tax mix as revenues from social security contributions and consumption taxes fall, and corporate tax collections remain low, according to a new OECD report. Revenue Statistics 2017 shows that, on average, OECD countries are becoming more reliant on personal income tax (PIT) revenues, with social security contributions (SSCs) and taxes on goods and services declining as a share of total tax revenue. The average share of PIT in total taxation increased from 24.1% in 2014 to 24.4% in 2015, while the respective shares of SSCs and taxes on goods and services (including VAT) fell slightly, according to the report. Corporate income taxes, which fell significantly during the financial crisis, have not recovered, remaining flat at around 8.9% of revenues. OECD (2017), Revenue Statistics: 1965-2016, OECD Publishing, Paris. www.oecd.org/tax/revenue-statistics-2522770x.htm OECD Guidelines on Measuring Trust Trust, both interpersonal trust, and trust in institutions, is a key ingredient of growth, societal well-being and governance. As a first step to improving existing measures of trust, the OECD Guidelines on Measuring Trust provide international recommendations on collecting, publishing, and analysing trust data to encourage their use by National Statistical Offices (NSOs). The Guidelines also outline why measures of trust are relevant for monitoring and policy making, and why NSOs have a critical role in enhancing the usefulness of existing trust measures. Besides looking at the statistical quality of trust measures, best approaches for measuring trust in a reliable and consistent way and guidance for reporting, interpretation and analysis are provided. A number of prototype survey modules that national and international agencies can use in their household surveys are included. OECD (2017), OECD Guidelines on Measuring Trust, OECD Publishing, Paris. www.oecd.org/std/oecd-guidelines-on-measuring-trust-9789264278219-en.htm

OECD Science, Technology and Industry Scoreboard 2017 The digital transformations With some 200 indicators, the 2017 edition of the OECD Science, Technology and Industry (STI) Scoreboard shows how the digital transformation affects science, innovation, the economy, and the way people work and live. It aims to help governments design more effective science, innovation and industry policies in the fast-changing digital era. The charts and underlying data in this publication are available for download and over half the indicators contain additional data expanding the time and/or country coverage of the print edition. OECD (2017), OECD Science, Technology and Industry Scoreboard 2017: The digital transformation, OECD Publishing, Paris. www.oecd.org/science/oecd-science-technology-and-industry-scoreboard-20725345.htm

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Recent publications Health at a Glance 2017: OECD Indicators This new edition of Health at a Glance presents the most recent comparable data on the health status of populations and health system performance in OECD countries. Where possible, it also reports data for partner countries (Brazil, China, Colombia, Costa Rica, India, Indonesia, Lithuania, Russian Federation and South Africa). The data presented in this publication come from official national statistics, unless otherwise stated. This edition contains a range of new indicators, particularly on risk factors for health. It also places greater emphasis on time trend analysis. Alongside indicator-by-indicator analysis, this edition offers snapshots and dashboard indicators that summarise the comparative performance of countries, and a special chapter on the main factors driving life expectancy gains. OECD (2017), Health at a Glance 2017: OECD Indicators, OECD Publishing, Paris. www.oecd.org/health/health-at-a-glance-19991312.htm

PISA 2015 Results (Volume V) Collaborative Problem Solving The OECD Programme for International Student Assessment (PISA) examines not just what students know in science, reading and mathematics, but what they can do with what they know. Results from PISA show educators and policy makers the quality and equity of learning outcomes achieved elsewhere, and allow them to learn from the policies and practices applied in other countries. PISA 2015 Results (Volume V): Collaborative Problem Solving, is one of five volumes that present the results of the PISA 2015 survey, the sixth round of the triennial assessment. It examines studentsâ&#x20AC;&#x2122; ability to work with two or more people to try to solve a problem. The volume provides the rationale for assessing this particular skill and describes performance within and across countries. OECD (2017, PISA 2015 Results (Volume V): Collaborative Problem Solving, OECD Publishing, Paris. www.oecd.org/education/pisa-2015-results-volume-v-9789264285521-en.htm

Road Safety Annual Report 2017 The IRTAD Road Safety Annual Report 2017 provides an overview of road safety performance for 2015 in 40 countries, with preliminary data for 2016, and detailed reports for each country. It includes tables with cross country comparisons on key safety indicators. The report outlines the most recent safety data in IRTAD countries, including detailed analysis by road user, age group and type of road. It describes the crash data collection process in IRTAD countries, the road safety strategies and targets in place, and information on recent trends in speeding, drinkdriving and other aspects of road user behaviour. The 2017 edition of the IRTAD Road Safety Annual Report puts special emphasis on road safety for an ageing population, which represents a growing concern in many countries. ITF (2017), Road Safety Annual Report 2017, Paris. www.itf-oecd.org/road-safety-annual-report-2017

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The Statistics Newsletter

for the extended OECD statistical network Issue 67 - November 2017 http://oe.cd/statisticsnewsletter To receive the OECD Statistics Newsletter by email, you can subscribe to OECDdirect e-mails: www.oecd.org/about/publishing/oecddirect.htm

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