The OECD Statistics Newsletter, December 2019, Issue 71 by The OECD Statistics and Data Directorate

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

FEATURING ++Gimme affordable shelter: building an OECD housing strategy ++Itâ&#x20AC;&#x2122;s time for a change! ++Identifying environmentally related tax revenues in Revenue Statistics

THE LATEST OECD AFFORDABLE HOUSING DATABASE OECD TAX-BENEFIT MODEL oe.cd/statisticsnewsletter Issue No. 71, December 2019

Contents 3

Gimme affordable shelter: building an OECD housing strategy

Boris Cournede (boris.cournede@oecd.org), Peter Hoeller (peter.hoeller@oecd.org) and Åsa Johansson (asa.johansson@oecd.org), Economics Department, OECD

It’s time for a change!

Peter van de Ven (peter.vandeven@oecd.org), Statistics and Data Directorate, OECD

Identifying environmentally related tax revenues in Revenue Statistics Miguel Cárdenas Rodríguez (miguel.cardenasrodriguez@oecd.org), Ivan HašČiČ (ivan.hascic@oecd.org),

Environment Directorate, OECD, Michelle Harding (michelle.harding@oecd.org), Centre for Tax Policy and Administration, OECD

Measuring international trade price indices

Guannan Miao (guannan.miao@oecd.org) and Fons Strik (fons.strik@oecd.org), Statistics and Data Directorate, OECD

SEEA experimental ecosystem accounting: towards mainstreaming ecosystems into policy and decision-making

Alessandra Alfieri, Department of Economic and Social Affairs, United Nations and Bert Kroese, Statistics Netherlands and Peter van de Ven, Statistics and Data Directorate, OECD

Recent publications

Forthcoming meetings

The Statistics Newsletter is published by the OECD Statistics and Data Directorate. This issue and previous issues can be downloaded from http://oe.cd/statisticsnewsletter To receive the OECD Statistics Newsletter by email, you can sign up at https://oe.cd/statsnews-signup Follow us on

@OECD_STAT

Editor-in-Chief: Martine Durand Editors: Nadim Ahmad and Peter van de Ven Editorial and technical support: Martine Zaïda and Sonia Primot Contact us at SDD.CommTeam@oecd.org

2 The OECD Statistics Newsletter - Issue No. 71, December 2019

Gimme affordable shelter: building an OECD housing strategy Boris Cournede (boris.cournede@oecd.org), Peter Hoeller (peter.hoeller@oecd.org) and Åsa Johansson (asa.johansson@oecd.org), Economics Department, OECD

Number of years of annual income needed to buy a 60 square meter flat in the country's capital city or financial centre, for a median income couple with two children, OECD average 12

10 8 6 4

6.8

7.4

1985

1995

10.3

10.2

2005

2015

2 0

Source: OECD (2019), Under Pressure: The Squeezed Middle Class, OECD Publishing, Paris, https://doi.org/10.1787/689afed1-en

Housing market bubbles are at the root of many financial crises. During 2007-08, house prices collapsed in several countries, marking the onset of the global financial crisis (see Figure 2). In the past few years, house prices have been increasing rapidly in many countries, and they are now above their pre-crisis level. In addition, the growing importance of housing in household balance sheets raises the sensitivity of aggregate consumption and investment to changes in house prices. Housing policies also influence economic developments through their impact on the mobility of workers, access to quality jobs and education.

Annual real percentage change, OECD average House prices

GDP

2016

2013

2010

-6

2007

-6

2004

-4

2001

-2

-4 1998

-2

1995

1992

1989

1986

1983

1980

1977

1974

1971

Figure 1. The cost of buying a flat in large cities has increased considerably

Figure 2. House price and business cycles are tightly linked

Source: OECD (2019), OECD Economic Outlook: Statistics and Projections (database), https://doi. org/10.1787/eo-data-en and OECD Analytical House Price database

Furthermore, housing developments affect environmental outcomes, including through interactions with urban land-use patterns, residential energy consumption and transport systems. Housing is a fundamental driver of the accumulation and the distribution of wealth and debt within and across generations. Housing market developments influence the distribution of income, consumption and wealth, housing being one of the largest spending items in household budgets and often the largest asset in household balance sheets (see Figure 3). Figure 3. Housing is the main asset of the middle class Annual real percentage change, OECD average Housing (main residence) Other real assets Deposits

Other real estate Other financial assets

100% 90% 80% 70% 60% 50% 40% 30%

20% 10% 0%

DEU AUT USA CAN FRA NLD GBR FIN AUS OECD IRL BEL LUX PRT NOR ITA EST GRC LVA ESP SVN POL HUN SVK

ousing is crucial for well-being and inclusiveness. Rising house prices undermine housing affordability, particularly for low-income households and in fastgrowing urban areas. The cost of buying a flat in large cities has increased by 50% since the mid1980s (see Figure 1). Access to affordable housing is also crucial for achieving a number of key policy objectives, including poverty and homelessness reduction, equality of opportunities and sustainable growth. Access to affordable housing also matters when it comes to addressing structural shifts in housing demand due to an ageing population.

Source: OECD (2019), “Housing, wealth accumulation and wealth distribution: evidence and stylized facts”, OECD Economics Department Working Paper, forthcoming

Issue No. 71, December 2019 - The OECD Statistics Newsletter 3

How does policy influence housing? Tackling the challenge policy-makers face in delivering affordable quality housing, while addressing financial stability risks and economic efficiency goals, requires the development of a multi-pronged housing policy strategy. It requires addressing issues that cut across many policy areas, including social housing, urban land use regulation, financial regulation, taxation, local public finance, welfare support, transport policies, housing standards, rental regulation and the enforcement of competition in related activities (e.g. construction, real estate). Some issues are specific to regions or groups of countries. For example, in many central and eastern European countries, formerly government-owned dwellings were sold to individuals or households who are not able to maintain the buildings or up-grade their energy efficiency. Moreover, the growing importance of digital platforms in the housing market has implications for efficiency and welfare. While platforms have reduced the entry costs for providers of short-term accommodation, they may reduce supply of affordable housing and drive up house prices and rents. This has raised new regulatory issues in a number of cities. A horizontal project on housing For the above reasons, the OECD has started a new horizontal project on housing. The objective of the project is to develop a framework, indicators and a policy toolkit to help governments design and implement coherent policy strategies. The strategies should ensure that housing sector developments are consistent with policy goals such as a better functioning housing market in terms of housing supply, quality, affordability, poverty reduction, access to public services, labour market efficiency, economic resilience and a clean urban environment. The framework will highlight potential trade-offs and synergies between policy goals when designing housing policies. New indicators to be assembled as part of the project include more granular, internationally comparable data on house prices, including across regions within countries, rents and land-use regulations, building on earlier OECD work. Household wealth surveys will also be used to measure the contribution of housing to gross and net wealth inequality between different socioeconomic groups. The key outcomes due to improved policy settings include greater resilience at the macro and micro levels (i.e. preventing excessive mortgage leverage at the household level), the removal of

4â&#x20AC;&#x192; The OECD Statistics Newsletter - Issue No. 71, December 2019

policy-related obstacles to labour mobility, reductions in housing-related inequalities and improved environmental quality. The indicators identified as most pertinent will enrich the housing dimension of the OECDâ&#x20AC;&#x2122;s work on measuring well-being. What are the challenges that the project will focus on? The project will take a cross-cutting approach on how policies can enhance housing outcomes at the country, regional and household level across a range of areas. Housing, economic resilience and performance The housing sector has been experiencing boom and bust episodes. The combination of inelastic housing supply together with the very elastic lending capacity of financial institutions makes the housing sector particularly prone to price bubbles and painful corrections. These characteristics of the housing market are often exacerbated by policy distortions. In a majority of countries, the tax treatment of housing, which is biased in favour of both homeownership and debt financing, is conducive to excessive mortgage borrowing and leverage. At the same time, regulations, including those related to land use, and other distortions (e.g. lack of competition in construction) that restrict the supply of housing further contribute to fuelling house prices beyond levels that would be consistent with underlying factors. This limits access to affordable housing. The work in this area will scrutinise the evidence to identify multidimensional policy actions, in an effort to achieve greater economic resilience and more affordable housing. Housing, labour mobility and the modern job market The ease of geographically moving residence has implications for the functioning of the labour market, as it affects the job-matching process and use of human resources. Policies magnifying the cost of moving, which range from high transaction taxes to uneven access to preferentially priced housing, including social housing, can trap workers in unemployment or low-productivity jobs. This poses an obstacle to the mobility of workers towards dynamic markets and, as a result, affects economic efficiency. The already high economic and human cost of such obstacles to mobility may rise in the coming decades, as the transformation of labour markets increases the premium on mobility and adaptability. Housing and inequality Housing outcomes and policies have distributional effects and can in particular influence equality of opportunities through access to quality jobs, schools and public services. Housing can therefore have a

significant impact on social mobility within and across generations. Policy drivers of housing outcomes are multiple. They include housing-related taxation, public cash and in-kind transfers through housing allowances and the availability of social housing, monetary policy and institutional features of credit markets, and the regulation of rental markets and of land use. All have implications for access to adequate housing and the distribution of income and wealth across households.

are currently being collected. The work will subsequently be expanded towards the collection of internationally comparable statistics on sub-national house price levels. Housing questionnaire

To underpin the policy analysis of the housing market, the OECD Questionnaire on Affordable and Social Housing (QuASH) is being updated and extended. The QuASH questionnaire collects qualitative and Decent, affordable quantitative information on outcomes Housing and the environment housing is essential for and policies to promote affordable Efficient, climate-compatible, green and social housing in OECD countries people’s well-being housing policies are at the core of and a sustainable and (such as spending, eligibility for social meaningful progress towards achieving housing, and benefit amounts). The inclusive economy. Sustainable Development Goals 2019 wave of the questionnaire covers No. 11 related to the creation of sustainable cities and a broad set of housing-related policies, including rental communities, and No. 13 on taking climate action. regulation and transaction costs. Policies such as quantitative restrictions, including on building height, urban growth boundaries and other A blueprint for achieving an inclusive housing strategy types of zoning can render housing less affordable and influence urban sprawl, with consequences for energy use and pollutant emissions. The OECD’s integrated land Decent, affordable housing is essential for people’s use and transport model can be extended and tailored well-being and a sustainable and inclusive economy. to shed light on these effects, contributing to the design Governments have a critical role to play, by putting in of integrated housing strategies that fully incorporate place the right policies at the national and local level. the environmental dimension. Leveraging its unique capacity to bring together expertise and insights, the OECD’s new work on housing will help Housing is local by nature countries enhance the effectiveness and coherence While some housing-relevant policies are set across of their housing policies and improve the well-being of the economy (such as mortgage market regulations), citizens. many policy decisions that drive housing outcomes are set at the local level. A key example is land-use The key outputs of the project include indicators and policy, which is overwhelmingly under the purview of evidence on the effects of policy on housing-related local governments, whereas rental-market regulation outcomes, advice on housing policies tailored to countryand housing-related taxation are also often under the specific circumstances, thematic reports on specific control of local authorities, but to a varying extent. Landaspects of housing, as well as policy briefs and notes. use regulations indeed play an important role in driving house prices, especially in the most expensive cities. The horizontal project intends to increase knowledge on the 1. www.imf.org/external/np/seminars/eng/dgi/index.htm local drivers of housing outcomes, so that OECD advice 2. https://ec.europa.eu/eurostat/documents/3859598/5925925/KS-RA-12- in the area of housing can mobilise local policy levers as 022-EN.PDF part of integrated strategies across government levels. 3. https://stats.oecd.org/Index.aspx?DataSetCode=HOUSING

House price data As a result of the G20 Data Gaps Initiative1, the OECD participated in the drafting of international statistical guidelines on residential property price indices (RPPIs)2, and now disseminates internationally comparable information on house price developments and related indicators at the national level3. Nevertheless, as noted before, many policy decisions that drive housing outcomes are set at the local level. To support these analyses, additional sub-national house price indices with metadata regarding their construction and coverage

Issue No. 71, December 2019 - The OECD Statistics Newsletter 5

It’s time for a change! Peter van de Ven (peter.vandeven@oecd.org), Statistics and Data Directorate, OECD

“GDP is an indicator of a society’s standard of living, but it is only a rough indicator because it does not directly account for leisure, environmental quality, levels of health and education, activities conducted outside the market, changes in inequality of income, increases in variety, increases in technology, or the—positive or negative—value that society may place on certain types of output”.1

his quote is one example from a vast and growing literature trying to dethrone GDP growth as the ultimate objective for policy. It is instructive to address some of the basic misconceptions that often permeates this sometimes quite heated debate. Firstly, although often used and interpreted as such, GDP growth is not a measure of changes in people’s (economic) well-being and, even less, its sustainability. This is well recognised in the 2008 System of National Accounts (SNA 2008)2: “GDP is often taken as a measure of welfare, but the SNA makes no claim that this is so and indeed there are several conventions in the SNA that argue against the welfare interpretation of the accounts”. In this sense, David Pilling (2018), quoting Terry Ryan, the chairman of the National Bureau of Statistics in Kenya, hits the nail on the head: “(GDP) … is not a meaningless indicator, but you have to understand what its meaning is”.3 As an indicator of (monetary) economic activity, GDP actually does a pretty good job; but when it comes to measuring people’s well-being, it has many clear and recognised limitations.

6 The OECD Statistics Newsletter - Issue No. 71, December 2019

Secondly, the System of National Accounts is often seen primarily as just a vehicle to provide a view of (the volume growth of) GDP. But the system provides much more than that and goes well beyond GDP. The accounts by design provide a complete, consistent and systematic overview of all (monetary) transactions in an economy from which a variety of macro-economic indicators can be derived, including Gross National Income (GNI), household (adjusted) disposable income, household final consumption and saving, corporate profits and balance sheets, and many more. GDP is the most widely used indicator because of its central use in macro-economic policy-making, but for other policy areas, for example, tracking household material well-being, other indicators, such as household disposable income, come to the fore. In addition, and to a large extent underpinning much of the debate, is the, perhaps misplaced, view that measures of well-being can be captured with the holy grail of a single catch-all indicator in monetary terms; one that also takes into account the potential of present-day losses (or gains) to generate (or not) future well-being. The pursuit of such an indicator is likely to be a dead-end. Well-being is a multi-faceted phenomenon that goes beyond material conditions (what people have). As such, it can only be captured by a dashboard of indicators4. One could try to put a price tag on each aspect contributing to people’s well-being, but in a way this would also mean “economising”, and thereby implicitly devaluing, everything that is considered important in life, many of which are priceless. Frameworks for capturing well-being and sustainability “Anyone who believes exponential growth can go on forever in a finite world is either a madman or an economist”.5

Monitoring and analysing economic activities are important goals in their own right, but that should not lead to policies that exclusively and unconditionally beat to the drum of higher GDP. Growth of what? For what purpose? For whom? GDP growth cannot be the ultimate objective of a society. As many have said, we need a better navigation system to guide policy towards the goals of enhancing the well-being of all, without jeopardising its sustainability for future generations. It is therefore important to develop metrics that cast a wider net going well beyond traditional economic indicators. An example of this approach is provided by the OECD How’s Life? report, which provides a dashboard of indicators pertaining to a range of well-being outcomes (both their average values and their distribution) as well as to those resources that are critical for future wellbeing (e.g. via stocks, flows and risks of different types of capital). A further step in this approach is the OECD Better Life Index6, a communication tool that allows users to aggregate headline measures related to 11 dimensions of people’s life, based on the users’ own subjective choice of weights for each dimension, which generates a single yardstick of countries’ average wellbeing performance. Defining a broader framework “So it has come to this. The global diversity crisis is so severe that brilliant scientists, political leaders, ecowarriors, and religious gurus can no longer save us from ourselves. The military are powerless, but there may be one last hope for life on earth: accountants”.7 The above quote may look satirical, but the author is not trying to be. Instead, he is emphasising the importance of quantifying, in this case, the stocks and flows of ecosystems, in the absence of which environmental degradation will not truly be taken into account when designing policy8. This approach is related to the argument that GDP growth basically defines our notions of successful economies and whether life is getting better. As Gleeson-White9 argues, methods to summarise developments can have an impact on the goals we pursue. As an example, in addition to the success-story of GDP, she mentions the concept of “profit” that was derived, for the first time, from the double entry bookkeeping system developed in the golden years of Venetian trade in the 14th century. Or to put it differently: “What we measure affects what we do; and if our measurements are flawed, decisions may be distorted”.10 All of this puts a major responsibility on the statistical community to develop metrics that can guide policy

towards a better and more sustainable future: metrics which are well-founded, based on a conceptual and statistical framework, agreed on across various areas of expertise, convincing, and easy to communicate. Can we do better than disseminating dashboards of indicators? In the context of updating the 2008 SNA, work has started on designing a framework linking the various aspects of well-being and sustainability. This line of work aims to bridge the gap between the traditional national (economic) accounts and the emerging dashboards used for well-being and sustainability. Such a framework would make it possible to monitor, analyse and understand the inter-relations between the various aspects of well-being and sustainability, and to better understand the trade-offs and the win-wins between the various domains. For example, what’s the relationship between the output of the medical industry and the unpaid household activities on care for (non-) household members, and how do these relate to the health outcomes of people? How does all of this affect employment, distribution of income, and government finance? How does environmental pollution impact on people’s health? More generally, how can we improve health outcomes? Should we reduce environmental pollution, spend more money on prevention, develop pharmaceuticals, or improve medical techniques? Answering these types of questions requires a fundamental rethinking of the way that we bring our disparate statistics together; linking business statistics from the medical industry for example with administrative data on medical treatments, data on how people spend their time, and so on. In such a framework, spending on inputs, the outputs of medical goods and services, emissions to air and water, etc. would be put together with relevant outcome indicators. Obviously, all of this will not provide a one-to-one relationship between the policy measures taken to improve people’s health and the actual results in terms of outcomes, but it will help to monitor and analyse, for example, the various links and feedback loops of the relevant policy measures. As a point on the horizon, one would like to see the development of an overarching framework, in which statistics on economic, societal and environmental issues are integrated but not necessarily monetised, and in which one can easily “drill down” into micro-datasets to assess, for example, differences in health outcomes between population groups and sub-national territories. In such a framework, inputs and outputs, which are the natural starting points for policy making, could be brought together with a broader set of policy targets, i.e. the outcomes defined in the dashboards currently used for monitoring well-being and sustainability.

Issue No. 71, December 2019 - The OECD Statistics Newsletter 7

A practical way forward Defining a suitable conceptual framework for linking a broader set of indicators, and having it endorsed by the worldwide statistical community, is a demanding task, which will require a lengthy process.11 A more realistic goal in the short to medium term would be to agree upon a framework in which a number of “thematic accounts” are integrated and linked together with the traditional set of economic accounts. In the process of updating the 2008 SNA, three priority areas for research have been identified by the international community: digitalisation; globalisation; and well-being and sustainability. In respect of the latter, there is strong appreciation of the need to expand the scope of the SNA to include aspects of environmental-economic accounts; health accounts; accounts for education and human capital; accounts on unpaid household activities, or time use; and distributional information on household income, consumption, saving and wealth. Once defined and populated, it would not be necessary to compile all accounts at a quarterly or annual basis. Some accounts, for which structural developments are the primary focus, could be compiled every 2-3 years, depending on user demands and data availability. As a final point, one should acknowledge the importance of communication. Referring to the traditional set of national accounts as being the “central” or “core” framework, and to the measurement frameworks for other areas as being “satellite accounts” or “supplementary tables”, is not particularly helpful and would risk alienating the broader

community of experts and concerned citizens beyond economists. A new terminology is clearly needed.

1. Kahn Academy; see www.khanacademy.org/economics-financedomain/macroeconomics/gdp-topic/circular-econ-gdp-tutorial/a/ how-well-gdp-measures-the-well-being-of-society-cnx. 2. Paragraph 1.75 of the United Nations, European Commission, IMF, OECD, World Bank (2009), “System of National Accounts 2008”, New York. Available at: https://unstats.un.org/unsd/nationalaccount/docs/sna2008.pdf. 3. Pilling, D. (2018), “The Growth Delusion: Wealth, Poverty and the WellBeing of Nations”, Tim Duggan Books, New York. 4. See e.g. the November 2018 report of the High-Level Expert Group on the Measurement of Economic Performance and Social Progress (HLEG). Available at: www.oecd.org/statistics/measuring-economic-social-progress/ HLEG-reports.pdf. 5. Kenneth Boulding, United States Congress, House (1973) Energy reorganization act of 1973: Hearings, Ninety-third Congress, first session, on H.R. 11510, page 248. 6. See www.oecdbetterlifeindex.org. 7. Jonathan Watts, The Guardian, 28 October 2010. 8. See also the article “SEEA Experimental Ecosystem Accounting: Towards Mainstreaming Ecosystems into Policy and Decision-making”, included in this Statistics Newsletter. 9. Gleeson-White, J. (2011), “Double Entry – How the Merchants of Venice Created Modern Finance – and How Their Invention Could Make or Break the Planet”, Allen & Unwin, Sydney, Melbourne, Auckland, London. 10. Stiglitz, J. E., A. Sen, and J-P. Fitoussi (2009), “Report by the Commission on the Measurement of Economic Performance and Social Progress”, Paris. Available at: https://ec.europa.eu/eurostat/ documents/118025/118123/Fitoussi+Commission+report. 11. For more information on how such a conceptual framework could be designed, see e.g. Hoekstra, R. (2019), “Replacing GDP by 2030. Towards a Common Language for the Well-being and Sustainability Community”, Cambridge University Press, Cambridge.

.Stat Suite – An open source solution for official statistics Given similar needs and challenges faced by countries in developing their statistical information systems (SIS), the OECD created the SIS Collaboration Community over ten years ago to collaborate in the development of common solutions. Sharing knowledge, this cooperation has helped to mutualise costs in fostering common standards (SDMX) and driven co-innovation in the production of state-of-the-art open source solutions for official statistics. This year saw the release of the .Stat Suite, an ‘SDMX-native’ full open source platform, building on best practices in statistical data modelling, to cover the full data lifecycle (design, collect, process, disseminate). The .Stat Suite incorporates automation and advanced quality assurance features and enables producers and users to work around the same data, avoiding, in turn, inefficient fragmentation of tools and process. Visit https://siscc.org to find out more about the .Stat Suite and the Community.

8 The OECD Statistics Newsletter - Issue No. 71, December 2019

EIGE’S data on gender balance in EU corporate boardrooms Comparable data produced by the European Institute for Gender Equality (EIGE) show that there has been significant progress in the representation of women as key decision-makers in the largest publicly listed companies in the EU in the last nine years, with the share of women more than doubling to 28% in April 2019 from only 12% in October 2010. At the national level, the data reveal how effective policy intervention can be in achieving gender equality. In the six countries with binding quotas for example, female representation on boards now stands at 35%, 26 percentage points higher than in October 2010. Countries with softer measures have also seen significant increases, with the share of women rising to 27% from 14% over the same period (up by 13 percentage points). In stark contrast however, in countries where no action has been taken, female representation has shown little change in the last nine years, with shares remaining at around 15%.

Change in the share of women on boards of the largest listed companies in 28 EU countries between October 2010 - April 2019 by type of action taken Changes since Oct-2010

40%

EU-28

35%

Binding quota (BE, DE, FR, IT, AT, PT)

30%

Soft measures (DK, IE, EL, ES, LU, NL, PL, SI, FI, SE, UK)

28%

25%

No action (all other EU Member States)

27%

35%

+26 pp

+16 pp

+13 pp

20% 15%

+3 pp

15% 10%

No action

Soft Measures

Quota

EU-28

Apr-19

Oct-18

Apr-18

Oct-17

Apr-17

Oct-16

Apr-16

Oct-15

Apr-15

Oct-14

Apr-14

Oct-13

Apr-13

Oct-12

Apr-12

Oct-11

Apr-11

Oct-10

Source: European Institute for Gender Equality, Gender Statistics Database – largest listed companies Legislative gender quota targets: FR (40%), BE, IT & PT (33%), DE & AT (30%). “Soft measures” includes the EU countries with legislative quotas that are restricted to state-owned companies or applied without sanctions.

Download data •• Gender Statistics Database: https://eige.europa.eu/gender-statistics/dgs •• Largest listed companies: https://eige.europa.eu/gender-statistics/dgs/indicator/wmidm_bus_bus__wmid_comp_compbm/bar European Institute for Gender Equality (EIGE): https://eige.europa.eu

Issue No. 71, December 2019 - The OECD Statistics Newsletter 9

Identifying environmentally related tax revenues in Revenue Statistics 1

Miguel Cárdenas Rodríguez (miguel.cardenasrodriguez@oecd.org), Ivan HašČiČ (ivan.hascic@oecd.org), Environment Directorate, OECD, Michelle Harding (michelle.harding@oecd.org), Centre for Tax Policy and Administration, OECD

n addition to their function as a source of government revenues, environmentally related taxes are key policy instruments to influence environmental outcomes. Internalising the external environmental costs that remain unpriced by the market is a key element of cost-effective environmental policy.2 There is a broad consensus in the economics literature that taxes can allow this goal to be achieved in a manner that is environmentally effective, economically efficient and socially inclusive (provided possible negative distributional impacts on vulnerable households are addressed through targeted measures). Many countries also use these taxes in complement to other measures to address pollution externalities. The OECD has two important databases that can provide insights on this area – the OECD Policy Instruments for the Environment (PINE) database3 and Revenue Statistics4 – but with some differences between the two (see below). In 2018 and 2019, therefore, the OECD Environment Directorate and the OECD’s Centre for Tax Policy and Administration undertook an exercise to reconcile data on environmentally related taxes in the two databases5 and also with reference to Eurostat’s National Tax Lists.6 Identifying environmentally related taxes in Revenue Statistics A first step towards comparing the two databases is to identify environmentally related tax revenues (ERTRs) in the OECD’s Revenue Statistics, which classifies taxes according to the economic function of their base (income, property, provision or consumption of goods and services), rather than the sector of the economy that they apply to or the intended purpose of the tax. Environmentally related taxes are identified by considering the environmental relevance of tax bases (OECD, 2006), i.e. independent of the economic function of the base. The UN System of Environmental-Economic Accounting defines them as follows: “Environmentally related taxes are taxes whose tax base is a physical unit (or a proxy of it) of something

10 The OECD Statistics Newsletter - Issue No. 71, December 2019

that has a proven, specific, negative impact on the environment.” (United Nations et al., 2014) In practice, ERTRs cross-cut the standard tax classification used in Revenue Statistics and are not directly comparable to those in the System of National Accounts (SNA)7 and Revenue Statistics. However, the large majority of ERTRs are likely to be included in the disaggregated tax categories on production and imports in the SNA (e.g. excise taxes, car registration taxes, pollution taxes) and on goods and services in Revenue Statistics (e.g. excise taxes, recurrent taxes on motor vehicles, and revenues from trading permits). Reconciling environmentally related tax revenue data in Revenue Statistics and PINE Differences between Revenue Statistics and the PINE database arise primarily due to (i) a lack of suitable disaggregation of revenue streams in Revenue Statistics to allow ERTRs to be identified and (ii) generic labels of taxes in Revenue Statistics that do not permit a match to the more detailed description of environmentally related taxes in the PINE database. In order to reconcile these sources, the following approach is adopted: 1. Identify environmentally related taxes in Revenue Statistics and, for EU member countries, in National Tax Lists submitted to Eurostat (hereafter Eurostat-NTLs). 2. Construct correspondence tables between the list of taxes in OECD PINE and those identified in Step 1. The tables contain about 700 PINE-Revenue Statistics and PINE-NTL correspondences. 3. Compare the level of the revenue reported. The reconciliation exercise also allowed the inclusion of revenues from auctions of tradable permits, which had been previously excluded from the aggregates for most countries. In particular, this concerns the inclusion

Figure 2. Distribution of ERTRs over time in OECD countries

of the revenue from auctions of permits under the EU-Emissions Trading Scheme. Trends in environmentally related taxes in OECD countries8 ERTRs in OECD countries form an important part of total tax revenues, accounting for 5.1% of total tax revenues on a weighted average basis (6.9% on a simple average basis) in 2017; ranging from 2.8% in the United States to 12.5% in Slovenia and Turkey. Although the average for the OECD in 2017 was little different to the 1995 share, most (26) OECD countries saw a decline in ERTRs as a share of tax revenues, with an average decrease of 1.7 percentage points in these countries (Figure 1). Figure 1. Environmentally related tax revenues in OECD (1995-2017) % of total tax revenu 2017 0 SVN TUR LVA KOR ISR EST NLD GRC DNK ITA PRT IRL CZE MEX GBR POL FIN OECD-S LTU HUN AUS CHL AUT SVK NOR CHE ESP ISL OECD-W BEL FRA SWE LUX DEU JPN NZL CAN USA

2010 8

% of GDP

1995 12

2017 16

2010 2

1995 4

SVN DNK LVA ITA NLD GRC TUR FIN EST ISR KOR CZE PRT AUT HUN GBR POL FRA OECD-S OECD-S BEL NOR SWE ISL LTU SVK ESP AUS IRL LUX DEU OECD-W OECD-W CHE CHL JPN NZL MEX CAN USA

Source: OECD (2019c) Environment Statistics (database)

Only 10 OECD countries saw ERTRs, as a share of total taxation, increase over the period, with an average increase of 2.9 percentage points. The largest increases were seen in: •• Latvia, from 3.3% to 12.2% of total tax revenues, due to higher energy tax revenues; •• Estonia, from 2.7% to 8.8%, entirely from energy tax revenues increasing; and •• Turkey, from 7.2% to 12.5%, with roughly equal increases in energy and transport taxes.

% of GDP Interquartile range

OECD-S

Median

3.5

3.0 2.5

2.0 1.5

1.0 0.5

0.0

Source: OECD (2019c) Environment Statistics (database)

Similarly, as a share of GDP, ERTRs in the OECD as a whole in 2017, on a simple average basis, were similar to the 1995 rates. ERTRs increased as a share of GDP in the late 1990s to a high of 2.6% of GDP in 1998 and 1999 before decreasing relatively steadily until 2008, picking up in 2009, and slowly increasing since. Half of the OECD countries had ERTRs of between 1.7% and 2.9% of GDP in 2017, as shown by the interquartile range in Figure 2. The greatest share of ERTRs in OECD countries in 2017 comes from taxes on energy, predominantly on road fuels (accounting for 71% of total revenues on both a simple and weighted average basis, and over 50% of total ERTRs in all countries, Figure 3). Figure 3. Composition of ERTRs in OECD countries (2017) Energy

Transport

Pollution and resources

Total, Million USD 2010 PPP

POL LUX LTU EST MEX HUN ESP LVA DEU SVK SVN ITA SWE FRA CZE CAN GRC GBR CHL PRT OECD-S OECD-W FIN BEL TUR KOR JPN USA IRL AUS AUT NOR NLD ISL DNK ISR CHE NZL

24 030 902 1 568 1 082 24 723 6 290 28 507 1 722 58 874 3 224 2 839 69 879 9 832 58 374 8 630 16 748 8 380 61 819 4 834 7 535 807 242 6 590 10 615 61 386 47 520 65 102 121 229 5 142 18 922 9 701 6 948 27 250 315 9 812 7 498 7 225 2 195

100

Source: OECD (2019c) Environment Statistics (database)

Issue No. 71, December 2019 - The OECD Statistics Newsletter 11

On both a weighted and a simple average basis, there has been a shift towards taxes on energy following the recent financial crisis and away from transport taxes. While revenues from both dropped as a share of GDP in 2008 and 2009, revenues from energy taxes recovered to pre-crisis levels relatively quickly and have increased slowly since. Revenues from transport taxes have not recovered to the same extent. OECD countries have become more similar, in having a higher share of revenues from energy rather than transport taxes in 2017 compared to 1995. Taxes on pollution and natural resources represent only a small portion of ERTRs in most countries and on average. This could change in the future if countries start placing a greater emphasis on addressing water pollution, sustainable resource use or conservation of biodiversity. While comparisons of ERTRs in OECD countries provide a useful starting point for analysing the impact of environmental taxation, comparing only the levels of revenues does not provide the full picture of a country’s environmental policy as it does not provide information on the levels of tax rates or the exemptions applied. Other parts of the OECD PINE database, including information on tax rates and exemptions, allows deeper assessment of the environmental impacts of the taxes. In addition, governments may choose to implement environmental policies using a range of other instruments, including fees and charges, expenditures (both direct and subsidies) and regulation. References OECD (2019a), Environmentally related tax revenue accounts: OECD methodological guidelines in line with the SEEA. Draft report prepared for the Working Party on Environmental Information ENV/EPOC/WPEI(2018)6/ REV1 OECD (2019b), OECD Policy Instruments for the Environment (PINE) database, http://oe.cd/pine OECD (2019c), "Environmental policy: Environmental policy instruments", OECD Environment Statistics (database), https://doi.org/10.1787/data-00696-en OECD (2018), Revenue Statistics - Interpretative Guide, OECD, Paris, France, www.oecd.org/tax/tax-policy/ oecd-classification-taxes-interpretative-guide.pdf OECD (2017a), Environmental Fiscal Reform: Progress, Prospects and Pitfalls, OECD, Paris, France, www. oecd.org/tax/tax-policy/environmental-fiscal-reformG7-environment-ministerial-meeting-june-2017.pdf

12 The OECD Statistics Newsletter - Issue No. 71, December 2019

OECD (2006), The Political Economy of Environmentally Related Taxes OECD (2001), OECD Glossary of Statistical Terms: Taxes, https://stats.oecd.org/glossary/detail.asp?ID=2657 United Nations (2009), System of National Accounts 2008 United Nations et al. (2014), System of EnvironmentalEconomic Accounting 2012: Central Framework, https://unstats.un.org/unsd/envaccounting/seeaRev/ SEEA_CF_Final_en.pdf

1. This article presents a summary of the special feature published as chapter 2 of Revenue Statistics 2019 (www.oecd.org/tax/revenue-statistics-2522770x.htm), released on 5 December 2019. 2. For a more detailed discussion, see OECD (2017a). 3. The OECD Policy INstruments for the Environment (PINE) database, established in 1998, hosts a unique set of detailed information on more than 3500 environmental policy instruments in over 105 countries. PINE contains information on six types of market-based policy instruments (taxes, fees and charges, tradable permits, environmentally motivated subsidies, deposit refund schemes and voluntary agreements). For more information, see http:// oe.cd/pine. 4. Revenue Statistics (www.oecd.org/tax/revenue-statistics-2522770x. htm) provides harmonised and detailed data on tax revenues for OECD countries from 1965 to 2018, according to the classification set out in the OECD Interpretative Guide. More recently, the Revenue Statistics series has expanded to include three regional publications in Africa, Asia and the Pacific, and Latin America and the Caribbean. Data for over 95 countries is included in the Global Revenue Statistics Database (www.oecd.org/tax/tax-policy/global-revenue-statisticsdatabase.htm). 5. Related work has focused on developing the OECD methodological guidelines on compiling accounts of environmentally related tax revenues (ERTRs) in line with the System of Environmental Economic Accounting (OECD, 2019a). 6. Eurostat publishes revenue data for individual taxes imposed by 28 EU-member states from 1995 onwards, based on the European System of National and Regional Accounts (ESA 2010). Eurostat NTLs distinguish dif-ferent categories of taxes, including environmentally related taxes. 7. https://unstats.un.org/unsd/nationalaccount/docs/SNA2008.pdf 8. OECD-W refers to the weighted average and OECD-S to the simple ave-rage. For Canada, Korea and Israel, data for 2014 are used for subsequent years as data for later years are not available in the PINE database and ERTRs cannot be identified in Revenue Statistics with sufficient precision to be included. For the same reason, data for 2016 are used for 2017 in Australia and the United States.

The Benefits and Wages database and OECD Tax-benefit model Comparable indicators on social transfers and work incentives How do taxes and cash transfers shape family incomes? Does income support protect people from poverty? Does work pay? The Benefits and Wages database (http://oe.cd/taxben) and OECD tax-benefit model (TaxBEN) are unique sources of harmonised policy information and indicators designed to facilitate the analysis of tax and benefit systems and reforms. Bringing detailed descriptions of tax and benefit legal rules that apply to working-age families in OECD and EU countries, into a unified methodological framework, the OECD TaxBEN model calculates internationally comparable indicators on income adequacy and work incentives over time. An intuitive web interface allows users to calculate tax liabilities and benefit entitlements for a wide range of family and labour-market circumstances. The database is updated annually, and currently includes data from 2001 to 2018 for most OECD and EU countries. Results for 2019 will be available in early 2020. Net household income for low-paid earners (40% of the average wage)

One-earner couple with two children

Source: OECD Tax-Benefit model (http://oe.cd/taxben), 2018 policies.

Supporting the Production, Coordination and Use of Gender Statistics ASSESSING DATA AND STATISTICAL CAPACITY GAPS FOR BETTER GENDER STATISTICS

©?????????????

FRAMEWORK AND IMPLEMENTATION GUIDELINES

PARTNERSHIP IN STATISTICS FOR DEVELOPMENT IN THE 21ST CENTURY

As part of its collaboration with UN Women (https://paris21.org/supporting-genderstatistics), PARIS21 has developed a comprehensive framework and implementation guidelines to assess data and statistical capacity gaps linked to gender statistics. The framework, aimed at national statistical offices and other stakeholders, proposes methods, activities and tools for conducting national gender statistics assessments and supports the design of national strategies for the development of statistics (NSDS) (https://nsdsguidelines.paris21.org). The data gaps assessment process takes a holistic view that looks at practicalities (data supply), needs (data demand), across all stake-holders, drawing on the PARIS21 data-planning tool, ADAPT (https://paris21. org/advanced-data-planning-tool-adapt), whilst the capacity assessment relies on the PARIS21’s capacity development tool (CD4.0 - https://paris21.org/capacitydevelopment-40), which is applied through a set of four targeted questionnaires.

The PARIS21 methodology recognises the importance of widespread and active participation of a wide set of stakeholders involved in the production, communication and use of gender statistics. It devotes special attention to the use of gender statistics by lawmakers, media, private sector, and civil society organisations & recognises their role in evidence-based policy-making and monitoring, transparency and good governance. The framework is available at https://paris21.org/node/3286.

Issue No. 71, December 2019 - The OECD Statistics Newsletter 13

Measuring international trade price indices Guannan Miao (guannan.miao@oecd.org) and Fons Strik (fons.strik@oecd.org), Statistics and Data Directorate, OECD

espite the growing importance of international trade, driven in large part by the rise of globalisation and the accompanying international fragmentation of production, the availability of statistics on price changes in international merchandise trade can, at best, be described as patchy. Although many countries provide measures of overall price changes in international trade, very little information exists at a more granular level. To fill this gap the OECD has developed a new database – the OECD Merchandise Trade Price Index database (MTPI). The first release covers about 100 countries from 2011 to 2017. Indices by reporting country are available for both exports and imports, broken down by 30 products, aligned with the 2-digit level of the Classification of Products of Activity (CPA, version 2.1). Future releases are planned to expand the country coverage and the level of disaggregation. What is new about the OECD’s approach? This is not the first effort to develop measures of price changes in merchandise trade statistics at the product level using Unit Value Indices (UVIs) but it is the first to mainstream these measures as a permanent activity and a regularly updated database. The approach builds on previous efforts, and refines them through

the incorporation of a multi-tiered process that reflects three-stages of outlier removal (see Figure 1). In theory, the creation of UVIs for a given product is a fairly trivial exercise, because customs data typically provide nominal and quantity values of imported or exported goods (by partner). The practice, however, is somewhat different as the alignment of quantity and value information often highlights administrative errors (mainly in the quantity) as well as inconsistent units (kg, tonnes, numbers, etc.) for quantities over time. Although quality changes in the underlying traded goods are not explicitly adjusted for, meaning that our measures of price changes are likely to be upward biased (and measures of volume change downward biased), the significant number of products (over 5000) used in the construction of our price indices mitigates some of the impact at the higher level of aggregation (especially at the 2-digit CPA level). Validating the Merchandise Trade Price Index Wherever possible the UVI estimates are validated and compared with existing data, including implicit price deflators from national accounts, official price indices at the Standard International Trade Classification (SITC) 1-digit level, and global commodity prices. Overall, the

Figure 1. Three-stage outlier removal process

1. Cross-section Prices •• Pool all 6-digit products within the same heading (4-digit), by quantity unit and by year •• Use AFM and revised MAD methods* to identify outliers of unit values in USD

2. Time series Price changes •• Remove flows with excessive year-on-year unit value changes (in USD and national currency), by reporting country and trading partner

3. Cross-section Price changes •• Pool all 6-digit products within the same heading (4-digit), by quantity unit and by year •• Use AFM and revised MAD methods* to identify outliers in unit value changes (in USD and national currency)

*The Asymmetric Fence Method (AFM) and revised Mean Absolute Deviation (MAD) outlier identification methods are most suitable when data distributions are not normal. AFM is recommended for data with more than 100 observations and MAD for data with less than 100 observations.

14 The OECD Statistics Newsletter - Issue No. 71, December 2019

Figure 2. World Bank commodity price index vs OECD MTPI

estimated UVIs align well with these statistics. About 85% of all observations have less than a 10% difference with the implicit price indices in national accounts, with a maximum absolute difference of 0.3 percentage points. A comparison with the World Bank commodity price data also reveals only small differences. Figure 2 provides an example across four selected commodities â&#x20AC;&#x201C; Cocoa, Copper, Crude Oil and Cotton.

price effects shows that the volume of imports picked up, albeit marginally, in 2015 compared to a significant fall in nominal terms (see Figure 3). On the other hand a focus on volumes shows that the pace of contraction was higher in 2016 (minus 6.9%) compared to nominal terms (minus 2.7%). The main value of the dataset lies in its ability to provide more granular insights. Figure 4 illustrates one typical

Figure 3. A focus on current prices reveals only part of the story: annual import growth in nominal and real terms (2012-2016)

What new insights can the Merchandise Trade Price Index database provide? There has been significant commentary in recent years, and certainly post-crisis, around the slowdown in trade and the slowdown in the pace of Global Value Chain expansion. This commentary typically focuses on nominal values, not volumes. Price effects, for example declining oil prices and other commodities, may be important explanatory factors to changes on nominal values. Adjusting for

Issue No. 71, December 2019 - The OECD Statistics Newsletterâ&#x20AC;&#x192; 15

Figure 4. Impacts of upstream price shocks are hard to predict: unit value indices by country for metal ores (import price index), basic metals, and fabricated metals (export price index) (2010-2016)

example looking at how price changes in upstream parts of value chains have an impact on the downstream. It shows a downward trend in the price change of metal ore imports in most countries but slower price falls (and sometimes increasing prices) in the exports of those sectors with high dependency on these imports (basic and fabricated metals), suggesting that manufacturers were able to profit from price falls, or that other costs, including labour, rose. Whatâ&#x20AC;&#x2122;s next? The plan over the next two years is to increase the level of granularity included in the database and to develop new analytical products based on the database. Efforts

are also being targeted at developing similar indices for services trade. Where to find the underlying data? OECD Merchandise Trade Price Index Database (MTPI), by 2-digit CPA, http://stats.oecd.org/Index. aspx?DataSetCode=MTPI_CPA Further reading Miao, G., Cheptitski, A., Strik, F. & Fortanier, F. (forthcoming, 2020). International merchandise trade price indices. OECD Statistical Working Paper Series.

OECD Affordable Housing Database Access to good-quality affordable housing is a fundamental need and key to achieving a number of social policy objectives, including reducing poverty, enhancing equality of opportunity and inclusive growth. However, housing needs are frequently unmet. Today a significant number of people across the OECD countries are homeless, and too many households live in low-quality dwellings or face housing costs they can barely afford. The OECD Affordable Housing Database (AHD) provides comparable indicators across OECD countries, EU member states, and some accession and key partner countries to support policy makers in assessing housing outcomes and identifying best housing policy practices. The database currently includes indicators grouped along three main dimensions: housing market context, housing conditions, and public policies towards affordable housing. Each indicator is also supported by meta-data describing definitions, methodology comparability, and sources. Access the Database and see the latest updates at http://oe.cd/ahd

16â&#x20AC;&#x192; The OECD Statistics Newsletter - Issue No. 71, December 2019

SEEA experimental ecosystem accounting: towards mainstreaming ecosystems into policy and decision-making Alessandra Alfieri, Department of Economic and Social Affairs, United Nations and Bert Kroese, Statistics Netherlands and Peter van de Ven (peter.vandeven@oecd.org), Statistics and Data Directorate, OECD

iodiverse, healthy ecosystems provide essential contributions that humans depend upon in their daily lives, such as clean water, productive soils, and ďŹ&#x201A;ood control to name just a few. But these contributions have too often been taken for granted in policy making. The resulting overexploitation, habitat destruction and pollution of the natural world has created profound damage to our biosphere, with the impact typically felt most by the poorest and most vulnerable in society who rely on biodiverse and healthy ecosystems for their daily needs. This calls for a better accounting for the economyâ&#x20AC;&#x2122;s dependence on the environment so that nature and its benefits appear on the ledger through the development of Natural Capital Accounting (NCA), thus recognising the environment as an asset which must be maintained and managed and which delivers essential ecosystem services. System of Environmental Economic Accounting (SEEA) The System of Environmental Economic Accounting (SEEA - https://seea.un.org) is the accepted international

statistical standard for NCA, and provides a framework for organising and presenting statistics on the environment and its relationship with the economy. It brings together economic and environmental information in an internationally agreed set of standard concepts, definitions, classifications, and accounting rules for compiling internationally comparable statistics. Two different perspectives are embodied in the SEEA. The first emanates from the viewpoint of the economy, and accounts for how natural resources like mineral and energy resources, fish stocks, forests and water are used in production and consumption, along with the resulting impact on the environment in the form of depletion of natural resources, generation of waste, and emissions to air and water. This perspective, based on the concept of environmental assets, is elaborated in the SEEA Central Framework (SEEA CF - https://seea.un.org/sites/seea. un.org/files/seea_cf_final_en.pdf), and was adopted by the United Nations Statistical Commission (UNSC) as the first international standard for environmental-economic accounting in 2012. However, the interactions between nature and the economy extend well beyond the harvesting, extraction and use of natural resources, and any resulting pollution

Figure 1. Implementation of the SEEA Central Framework around the world

Yes No No, but planning to No data

Issue No. 71, December 2019 - The OECD Statistics Newsletterâ&#x20AC;&#x192; 17

and depletion of natural resources. Therefore, a second perspective, looking at ecosystems, is provided by the SEEA Experimental Ecosystem Accounting (SEEAEEA). This complements the Central Framework by considering how individual environmental assets interact as part of natural processes within a given spatial area. Implementation of the SEEA Central Framework (SEEA CF) The SEEA Central Framework is a relatively new standard that complements the System of National Accounts by providing insights on the state of the environment and its links with the economy and well-being more generally. It is increasingly being implemented in countries and mainstreamed in the regular production process of national statistical offices. Figure 1 shows that 90 plus countries are now implementing the SEEA CF. SEEA Experimental Ecosystem Accounting (SEEA EEA) Approximately 40 countries currently experimenting with the SEEA CF are also testing the SEEA EEA. Fundamental to ecosystem accounting is the recognition that ecosystems are the source of goods and services that are essential to economic prosperity and human well-being, now and in the future. An ecosystem is defined as “a dynamic complex of plant, animal and micro-organism communities and their non-living environment interacting as a functional unit”. Ecosystem assets are areas covered by specific ecosystem types, and include, for example, forests, wetlands, agricultural areas, rivers and coral reefs. The contributions of ecosystems range from natural products such as timber and game to services like purification of air and water, pollination of crops, nutrient cycling, flood mitigation, erosion control and carbon storage. The importance of these services underlines the need for a thorough

understanding of the ways in which ecosystems support economic and social well-being. SEEA EEA conceptual model The SEEA EEA is a coherent and harmonised framework for mainstreaming data on ecosystems into decisionmaking. It contains a detailed description of the way in which accounting for the stocks of ecosystem assets, including the flows leading to changes in stocks, can be organised. How should we define and delineate ecosystem assets? What are the measurement boundaries? Which accounting principles, account structures and classifications should be applied when integrating data on ecosystem assets with data on economic and other human activities? The framework, which is well aligned with national accounting principles, allows for the measurement of ecosystem assets in terms of both their condition (overall health) and the services they provide, and can be applied consistently across terrestrial, freshwater and marine areas. The accounts are also designed to generate consistent time series. A defining characteristic of ecosystem accounting is that it is spatially explicit, i.e., it builds accounts based on underlying maps with information. As such, ecosystem accounting produces an integrated spatial information system.

Ecosystem accounting is based on the conceptual model shown in Figure 2. The model starts with identifying ecosystem assets – an ecosystem that is mapped by mutually exclusive spatial boundaries in such a way that each asset is classified to a single ecosystem type. Assets can be described through their condition and extent. Through intra- and interecosystem flows, ecosystem assets generate ecosystem services – the contributions of ecosystems to benefits used in economic and other human activity, for example water regulation. These services provide benefits, which affect individual and societal well-being. SEEA EEA Figure 2. SEEA EEA conceptual framework typically distinguishes three types of ecosystem services: •• provisioning services (e.g. supply of food, fibre, fuel and water); •• regulating services (related to activities of filtration, purification, regulation and maintenance of air, water, soil, habitat and climate); and

18 The OECD Statistics Newsletter - Issue No. 71, December 2019

â&#x20AC;˘â&#x20AC;˘ cultural services (related to activities of individuals in, or associated with, nature, such as recreation). The main focus of ecosystem accounting is on the supply of final ecosystem services to economic units, including businesses and households . Most commonly, a single ecosystem asset will supply a basket of different ecosystem services. The intent in SEE EEA is to record the supply of all ecosystem services over an accounting period for each ecosystem within an accounting area. Furthermore, the SEEA EEA provides appropriate concepts and techniques for compiling ecosystem accounts in monetary terms that can be integrated with standard economic accounting data, by using valuation principles of the SNA. Policy applications The SEEA EEA comprises a set of building blocks. Individually and collectively, they provide important information for policy makers and other decision makers. As presented in Figure 3 below, the measurement of ecosystems in terms of their spatial area (extent) and overall health (condition), combined with their contributions to economic activity through the provision of distinct ecosystem services that produce benefits for identifiable beneficiaries, can provide very relevant input from an environmental perspective into policy and decision-making on national or subnational levels. Generating maps at two different points in time allows an assessment of changes in the extent of different ecosystems to be made (see Figure 4). Overlaying

ecosystem extent maps with information on, for example, protected areas or on land tenure, allows for an assessment of the impact of land tenure on ecosystems. Figure 4 shows changes in aggregated ecological condition for main rivers in South Africa. The main data source consisted of two comprehensive national assessments of Present Ecological State of rivers, based on four indicators of ecological condition: flow; water quality; instream habitat; and riparian habitat. The ecological condition was assessed for each river reach. Rivers indicated in purple indicate good condition as opposed to yellow that indicate poor condition. These results have supported policies in the National Water and Sanitation Master Plan, currently being developed by the South African Department of Water and Sanitation, which highlights the importance of maintaining the integrity of freshwater ecosystems as part of the water value chain. They also identify where decline in condition is most severe in order to target actions. The example from the UK shown in Figure 5 presents partial UK natural capital asset value estimates for 2016 by ecosystem service. As we can see, the largest value is attributed to recreation, followed by agriculture, carbon sequestration and fossil fuels. Please note that this is a partial value as many ecosystem services provided by natural assets in the UK are not yet estimated. More generally, the valuation of natural capital is important, as it allows for estimates of the degradation cost of natural capital caused by human activity. It is a component of the total wealth of a country, together with

Figure 3. Extent accounts for Mexico

Issue No. 71, December 2019 - The OECD Statistics Newsletterâ&#x20AC;&#x192; 19

Figure 4. Condition accounts for South Africa

As illustrated above, the SEEA EEA is particularly well suited to support important global environment and development initiatives, such as: •• Monitoring of the Sustainable Development Goals, particularly progress towards Goals 6, 11, 12, 13, 14 and 15; •• The regional and global assessments of the Intergovernmental SciencePolicy Platform on Biodiversity and Ecosystem Services (IPBES); •• The Post-2020 Global Biodiversity Framework of the Convention on Biological Diversity;

produced, financial, human and social capital, and can thus support sustainability analyses. SEEA EEA in summary Ecosystem accounting allows for the identification of economically critical ecosystem assets, ecosystem types and ecosystem services; the monitoring of the status (health/condition) of ecosystems; the evaluation of the effectiveness of various policies; and the incorporation of ecological information into economic and financial decision-making. Figure 5. UK Natural Capital Accounts 2019 Asset values in 2016 (£ million, 2018 prices) Agriculture

Fish capture

Fossil fuels

Minerals

Timber

Water abstraction

Renewables

Carbon sequestration

Air pollutant removal

Urban cooling

Recreation

20 The OECD Statistics Newsletter - Issue No. 71, December 2019

•• The development of wealth accounting encompassing measures of the value of natural capital (World Bank, UNEP); •• Supporting the Land Degradation Neutrality policy under the UN Convention to Combat Desertification (UNCCD); and •• Supporting the measurement of biodiversity and ecosystem services for The Economics of Ecosystems and Biodiversity (TEEB) project. The Revision of SEEA EEA The uptake of the SEEA EEA since it was first endorsed by the United Nations Statistical Commission in 2013 has been tremendous. Countries have started experimenting with the SEEA EEA, and collaboration among experts from different disciplines, including environmental accountants, environmental economists, ecologists, scientists, geospatial experts has contributed to quickly advancing the research agenda on ecosystem accounting and establishing best practices in its implementation. These experiences have given us sufficient confidence on the application and policy relevance of the methodology to drop the word “experimental” from the title of the revised SEEA EEA. A large group of multi-disciplinary experts is now contributing to the revision of SEEA EEA, with the objective to arrive at a real statistical standard by 2021.

Recent publications 2019 Partner Report on Support to Statistics (PRESS2019) On 26 September, PARIS21 launched the 2019 Partner Report on Support to Statistics (PRESS2019). PRESS2019 highlights an increase in investments to statistics, largely driven by the adoption of the Sustainable Development Goals (SDGs) monitoring framework, but notes that current levels are still only half of what they need to be to support SDG monitoring.

2019 Partner Report on Support to Statistics (PRESS2019), Paris21. https://paris21.org/sites/default/files/inline-files/PARIS21_Press%202019_WEB.pdf

Pensions at a Glance 2019 Governments should urgently reform their pension systems to ensure that the growing share of workers in temporary or part-time employment can contribute enough during their working lives to receive an adequate income in retirement, according to an OECD report. Pensions at a Glance 2019 says that non-standard employment, such as self-employment, temporary or part-time work, now accounts for more than one-third of employment across OECD countries. Part-time work is three times more frequent among women than among men and self-employment is particularly common among older workers. OECD (2019), Pensions at a Glance 2019: OECD and G20 Indicators, OECD Publishing, Paris. www.oecd.org/pensions/public-pensions/oecd-pensions-at-a-glance-19991363.htm

OECD Economic Outlook, November 2019 Trade conflict, weak business investment and persistent political uncertainty are weighing on the world economy and raising the risk of long-term stagnation, according to the OECD’s latest Economic Outlook. World GDP growth is expected to be 2.9% this year – its lowest annual rate since the financial crisis – and remain at 2.9%-3.0% in 2020 and 2021. Global GDP expanded 3.5% in 2018.nological change, some countries are better prepared than others as a result of the skill levels of their populations. OECD (2019), OECD Economic Outlook, Volume 2019 Issue 2, OECD Publishing, Paris. www.oecd.org/economic-outlook

Issue No. 71, December 2019 - The OECD Statistics Newsletter 21

Recent publications Government at a Glance 2019 Trust in governments has recovered to pre-crisis levels and satisfaction with public services has improved, as public sector reforms aimed at making governments more open, accountable and engaged start to bear fruit, according to an OECD report. Government at a Glance 2019, the sixth edition of the OECD’s two-yearly overview of public governance, compares OECD and partner countries in areas such as public spending, investment, public procurement, public sector employment and government openness using around 60 indicators. The report contains scorecards and results of citizen surveys on health, education and justice services, and is accompanied by 38 individual country factsheets. OECD (2019), OECD (2019), Government at a Glance 2019, OECD Publishing, Paris. www.oecd.org/gov/govataglance.htm

World Energy Outlook 2019 Deep disparities define today’s energy world: oil markets and geopolitical tensions, carbon emissions and climate targets, the promise of energy for all and the lack of electricity access for 850 million people around the world. World Energy Outlook 2019 explores these widening fractures in detail. It explains the impact of today’s decisions on tomorrow’s energy systems, and describes a pathway that enables the world to meet climate, energy access and air quality goals while maintaining a strong focus on the reliability and affordability of energy for a growing global population. World Energy Outlook 2019, IEA. www.iea.org/weo2019

Taxing Energy Use 2019 Taxing polluting sources of energy is an effective way to curb emissions that harm the planet and human health, and the income generated can be used to ease the low-carbon transition for vulnerable households. Yet 70% of energy-related CO2 emissions from advanced and emerging economies are entirely untaxed, offering little incentive to move to cleaner energy, according to an OECD report. As world leaders gather for a UN Summit on climate change amid mounting public pressure for action, a preview of Taxing Energy Use 2019 shows that for 44 countries accounting for over 80% of energy emissions, taxes on polluting sources of energy are not set anywhere near the levels needed to reduce the risks and impacts of climate change and air pollution. OECD (2019), Taxing Energy Use 2019: Using Taxes for Climate Action, OECD Publishing, Paris. www.oecd.org/tax/taxing-energy-use-efde7a25-en.htm

22 The OECD Statistics Newsletter - Issue No. 71, December 2019

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

OECD Date

Meeting

16 December 2019

DAC Working Party on Development Finance Statistics (WP-STAT), Development Co-operation Directorate, OECD, Paris, France Making Migration and Integration Policies Future Ready — Ministerial and Forum on Migration, Directorate for Employment, Labour and Social Affairs, OECD, Paris, France - www.oecd.org/ migration/ministerial Global Forum on Environment: Mainstreaming Gender and Empowering Women for Environmental Sustainability, Environment Directorate, OECD, Paris, France International Transport Forum, 7th Annual Statistics Meeting, OECD, Paris, France

16-17 January 2020

5-6 March 2020 18-20 March 2020 23-27 March 2020

Working Group on International Investment Statistics (WGIIS), Directorate for Financial and Enterprise Affairs, OECD, Paris, France 23-27 March 2020 Working Party on International Trade in Goods and Services Statistics (WPTGS), Statistics and Data Directorate, OECD, Paris, France 31 Mar.-1 Apr. 2020 Joint Session of the Tourism Committee and Working Party on Tourism Statistics, Centre for Entrepreneurship, SMEs, Regions and Cities, OECD, Paris, France 4-7 May 2020 Measurement of Statistical Capacities, Statistical Capacity Monitor Task Team, PARIS21, Statistics and Data Directorate, OECD, Paris, France 11-12 May 2020 Eurostat-OECD Workshop on Purchasing Power Parities, Statistics and Data Directorate, OECD, Paris, France 13 May 2020 4th Meeting of the Working Party for the OECD Patient Reported Indicator Surveys, Directorate for Employment, Labour and Social Affairs, OECD, Paris, France 25-29 May 2020 OECD Week, OECD, Paris, France 27-29 May 2020 ITF 2020 Summit: Transport Innovation for Sustainable Development, International Transport Forum, Leipzig, Germany - www.itf-oecd.org/itf-2020-summit-transport-innovation-sustainable-development 8-12 June 2020 Meeting of the Committee on Statistics and Statistical Policy (CSSP), Statistics and Data Directorate, OECD, Paris, France 29 June-1 July 2020 SIS-CC 10th Annual Workshop, Statistics and Data Directorate, OECD, Paris, France 24-25 September 2020 2nd Workshop on Time Series Methods for Official Statistics, Statistics and Data Directorate, OECD, Paris, France 7-9 October 2020 SDMX Meeting, Statistics and Data Directorate, OECD, Paris, France 19-21 October 2020 Meeting of the Working Party of National Experts on Science and Technology Indicators (NESTI), Directorate for Science, Technology and Innovation, OECD, Paris, France 19-23 October 2020 Regional TiVA – Extended Supply and Use Tables meeting, Statistics and Data Directorate, OECD, Paris, France 26-28 October 2020 Working Group on International Investment Statistics (WGIIS), Directorate for Financial and Enterprise Affairs, OECD, Paris, France 2-6 November 2020 Meeting of the Working Party on National Accounts, Statistics and Data Directorate, OECD, Paris, France

Other meetings 21-24 January 2020 18 April 2020 21-22 April 2020 6-8 May 2020 17 October 2020 21-22 November 2020

World Economic Forum Annual Meeting, Davos-Klosters, Switzerland Spring Meetings of the World Bank Group and the International Monetary Fund, Washington, D.C., United States Global Technology Governance Summit, San Francisco, United States - www.weforum.org/ events/global-technology-governance-summit-2020 World Economic Forum on Latin America, São Paulo, Brazil - www.weforum.org/events/ world-economic-forum-on-latin-america-2020 Annual Meetings of the World Bank Group and the International Monetary Fund, Washington, D.C., United States 15th Annual Summit of G20 Leaders, Riyadh, Saudi Arabia

Issue No. 71, December 2019 - The OECD Statistics Newsletter 23

The Statistics Newsletter

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