The OECD Statistics Newsletter, Issue 66, June 2017 by The OECD Statistics and Data Directorate

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

FEATURING ++Evidence from manufacturing business ++How to measure global statistical literacy ++Good practices for the follow up to the 2030 Agenda

THE LATEST SUPPLY AND USE TABLES NEW DATASETS

www.oecd.org/std/statisticsnewsletter Issue No. 66, June 2017

OECD COMPENDIUM OF PRODUCTIVITY INDICATORS 2017

Contents 3

Management and productivity: New evidence from manufacturing businesses in Great Britain Gaganan Awano, Alice Heffernan and Harriet Robinson, Ofﬁce for National Statistics, UK (Productivity@ons.gsi.gov.uk)

How to measure global statistical literacy? Thilo Klein, PARIS21, OECD Statistics Directorate (PARIS21@oecd.org)

EIGE’s gender statistics database European Institute for Gender Equality (EIGE) (eige.sec@eige.europa.eu)

Generating good practices for the follow-up to the 2030 Agenda: the Colombian experience Mauricio Perfetti, Chief Statistician, National Administrative Department of Statistics of Colombia (mperfetti@dane.gov.co)

Forthcoming meetings

Recent publications

The Statistics Newsletter is published by the OECD Statistics Directorate. This issue and previous issues can be downloaded from www.oecd.org/std/statisticsnewsletter To receive the OECD Statistics Newsletter by email, you can subscribe to OECDdirect e-mails: www. oecd.org/about/publishing/oecddirect.htm 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 September 2017

The OECD Statistics Newsletter - Issue No. 66, June 2017

Management and productivity New evidence from manufacturing businesses in Great Britain Gaganan Awano, Alice Heffernan and Harriet Robinson, Office for National Statistics, UK (Productivity@ons.gsi.gov.uk)

he role of managers is gaining prominence in economic literature in recent times. Managers are responsible for allocating resources in the most efficient manner in order to maximise output. The literature shows an association between higher prevalence of structured management practices in businesses and higher levels of labour productivity1. To better understand the United Kingdom’s (UK) persistent lag in productivity among most of its peers in the Group of 7 (G7) industrialised economies2, and the more recent stagnation in productivity growth – known as the “productivity puzzle” – the UK’s Office for National Statistics (ONS) launched a Management Practice Survey (MPS), to provide evidence on the role of management on productivity. This survey of manufacturing businesses in Great Britain finds a strong association between management practices and firm size in terms of employment. The results also show a strong association between higher prevalence of structured management practices and higher levels of productivity. Background of the survey In 2016, the ONS launched a pilot MPS to investigate the relationship between structured management practices

and labour productivity in manufacturing businesses with employment of 10 or more in Great Britain (England, Scotland and Wales). The voluntary postal survey had a small sample of 1,026 businesses, and achieved a final response rate of 68% – a total of 702 responses. The survey asked eight key questions on the use of structured management practices within the business, covering practices relating to key performance indicators, targets, hiring decisions, promotions, and managing staff under-performance (see Awano and Robinson (2016) for details)3. For these questions each business’ response was ranked between 0 and 1, where 0 indicated the least structured and 1 the most structured management practice, and the scores were averaged to derive an overall management score for the business4. Business structure and management scores The structure of our manufacturing population provides some insight into the relative impact of this study. In Figure 1 we show that most businesses in our population are small (74% with employment between 10 and 49). From another dimension, the majority of businesses (64%) have some kind of family ownership, and over half (55%) of the manufacturing population have a Managing Director who is either an owner or related to the owner, and 9% are family-owned but not managed by anyone

Figure 1. Share of manufacturing businesses by characteristic Great Britain, 2015

Multinational Status 6%

10%

Family Ownership

Size 7%

5% 36%

14%

55% 74%

84%

Domestic

10 to 49 employment

Family-owned and family-managed

UK Multinational

50 to 99 employment

Family-owned and non-family-managed

Non-UK Multinational

100 to 249 employment

Not family-owned

250 or more employment

Source: UK Office for National Statistics Note: Population of interest is manufacturing firms with employment of at least 10 in Great Britain.

Issue No. 66, June 2017 - The OECD Statistics Newsletter 3

related to the owning family. Lastly, businesses with only UK domestic operations account for 84% of the population. On a scale of 0 to 1, where 0 indicates the least and 1 the most structured management practices, we find the average management practice score for our manufacturing population to be 0.56, but varying by business characteristic. For instance, Figure 2 shows average scores by quintile for each employment sizeband. We observe that small businesses (10 to 49 employment), which account for a large share of the

population, have lower management scores than the population average across all quintiles. The largest variations in scores are among the bottom 20% of the size distributions, while the top 20% have less varied scores. The distribution of scores by ownership types (Figure 3) shows different trends among quintiles for the two groups with the highest management scores – non-family owned and family-owned but not-family-managed businesses. Family-owned-and-managed businesses which account for more than half (55%) of the population have the lowest

Figure 2. Average score by quintile and business size Great Britain, 2015 All manufacturing

10-49 employment

50-99 employment

100-249 employment

250+ employment

Management score 1.00 0.90 0.80 0.70 0.60 0.50 0.40 0.30 0.20 0.10 0.00

Quintile Source: Management Practice Survey – UK Office for National Statistics Note: Population of interest covers manufacturing businesses in Great Britain with employment of at least 10.

Figure 3. Average score by quintile and family ownership status Great Britain, 2015 All manufacturing

Not family-owned

Family-owned

3 Quintile

Management score

Family-owned and managed

Family-owned and non-family-managed

1.00 0.90 0.80 0.70 0.60 0.50 0.40 0.30 0.20 0.10 0.00

Source: Management Practice Survey – UK Office for National Statistics Note: Population of interest covers manufacturing businesses in Great Britain with employment of at least 10.

4 The OECD Statistics Newsletter - Issue No. 66, June 2017

scores across the quintile distribution, which are below the average for the manufacturing population as a whole. Management practice scores and productivity

practice scores and labour productivity. In column (1) of Table 1, we start by looking at management score and employment-size, controlling for industry, and find a strong positive correlation. We add other characteristics in column (2), and find that the relationship between management score and size remains similar, and no other relationships are significant.

By linking the MPS to the Annual Business Survey (ABS) – the UK’s Structural Business Survey, we are able to assess the relationship between management practice Next we examine the relationship between labour scores and the level of labour productivity productivity and management score The prevalence for each business (see Awano et al., in columns (3) to (5), starting with a 5 2017). Figure 4 shows that non-familysimple model and gradually adding of structured owned businesses and multinationals more variables, to examine the effect of management have higher average management additional characteristics on the strength practices vary across scores and higher levels of productivity of the association from the initial position. UK manufacturing compared to family-owned and domestic businesses. businesses. For multinational businesses In column (3), the unconditional the location of their parent company relationship between management seems irrelevant, with comparable management scores score and labour productivity shows that an increase and productivity levels among those with and without in management score of 0.10 is associated with an 8.6% UK parent companies: although higher relative levels increase in labour productivity. To put this in context, of productivity may, at least in part, reflect economies the distribution of management scores shows that there of scale as MNEs are typically larger than non-MNEs. is a 0.13 difference in score between the median and the 75th percentile, so this relationship translates to a difference in productivity of 11.1%.6 On the whole, our Estimating relationships between simplest model estimates that management score can management practice scores and productivity account for 8% of the variation in productivity across businesses in our sample. We use regression analysis to further examine the associations we observed between management Figure 4. Labour productivity and management score by family ownership Great Britain, 2015 OPW - all manufacturing (LHS) OPW by multinational status (LHS)

OPW by family ownership type (LHS) Management score (RHS) Management score

Output per worker (£000s)

0.8

0.7

0.6 40

0.5 0.4

0.3

0.2 10 0

0.1 All

FNM

MUK

MNUK

0.0

Source: UK Office for National Statistics Notes: 1. OPW is output per worker. 2. Labour productivity is measured as output per worker (GVA/employment) in 2015 current prices. 3. Population of interest covers manufacturing business in Great Britain with employment of at least 10. 4. Key: All = All manufacturing (for businesses with at least 10 in employment); NF = Not family-owned; F = Family-owned; FM = Family-owned and family-managed; FNM = Family-owned and non-family-managed; D = Domestic; M = Multinational; MUK = Multinational with head office based in the UK; MNUK = Multinational with head office based outside the UK.

Issue No. 66, June 2017 - The OECD Statistics Newsletter 5

Table 1. Regression analysis of management score and labour productivity at the business level Great Britain, 2015 (1)

(2)

(3)

(4)

(5)

Management score

Log(Output per worker)

Management score Log(employment)

0.855**

0.608**

0.669***

(0.312)

(0.290)

(0.226)

0.110***

0.108***

0.049*

0.047

(0.012)

(0.014)

(0.027)

(0.042)

Family-owned and familymanaged business Family-owned and nonfamily-managed business Multinational UK Multinational Age (years) Age squared

-0.006

-0.184***

(0.058)

(0.067)

0.047

-0.265***

(0.033)

(0.095)

0.004

-0.090

(0.043)

(0.105)

0.002

-0.080

(0.028)

(0.115)

-0.000

0.017

(0.012)

(0.068)

-0.000

(0.000) Industry group dummies

Yes

0.327

0.336

0.319

0.322

Adjusted R 2 Equal family p-value

0.115

Joint industry p-value

0.000

Observations

694

(0.002) No

Yes

0.079

0.184

0.216

0.077

0.171

0.195 0.507

591

0.000

591

Source: UK Office for National Statistics Notes: 1. Standard errors in parentheses are clustered by industry and employment size band, * p < 0.1, ** p < 0.05, *** p < 0.01. 2. All regressions use Ordinary Least Squares. A constant is also included in all regressions. 3. “Equal family p-value” is the probability that coefficients for “Family-owned and family-managed business” and “Family-owned and non-family-managed business” are equal, and “Joint industry p-value” is the probability that the coefficients for all the industry grouping dummies are zero. These probabilities are estimated using F-tests. 4. Labour productivity is measured as output per worker (GVA/employment) in current prices (for year 2015). 5. Population of interest covers manufacturing businesses in Great Britain with employment of at least 10. 6. Industry dummies are as follows: Food, beverages, tobacco (divisions 10 to 12); Textiles, wearing apparel, leather (divisions 13 to 15); Wood, paper products, printing (divisions 16 to 18); Chemicals, Pharmaceuticals, rubber, plastics, non-metallic minerals (divisions 20 to 23); Basic metals, metal products (divisions 24 to 25); Computer etc. products, electrical equipment (divisions 26 to 27); Machinery, equipment, transport equipment (divisions 28 to 30); Coke, petroleum, other manufacturing (divisions 19 and 31 to 33).

The model in column (4) controls for employment size and industry, and shows the relationship between management score and productivity at a lesser extent, with a 0.10 increase in management score related to a 6.1% increase in labour productivity. When we add variables for the business characteristics – that is, family ownership types, multinational status and business age (column 5), we find the strength of the association still holds and a 0.10 increase in management score is associated with a 6.7% rise in productivity. This translates to a difference in productivity of 8.7% between the median management score and the 75th percentile. Collectively, all factors included in the regression account for just over a fifth of the variation in productivity at the business level.

6 The OECD Statistics Newsletter - Issue No. 66, June 2017

In the final model in column (5), the results also indicate that both types of family-owned businesses have much lower productivity, when controlling for management score, size, industry, age and multinational status. Generally, we conclude that productivity is around 20% lower for these family-owned businesses. Conclusions and next steps This study has shown that the prevalence of structured management practices vary across UK manufacturing businesses, most notably varying by employment levels. We find a positive relationship between higher management practice scores and higher levels of productivity. We estimate that an increase in management score of 0.10 is associated with an increase

in labour productivity of 8.6%. Even when taking into account specific business characteristics, we find that an increase in management score of 0.10 is associated with a productivity increase of 6.7%. However, family-owned businesses perform poorly in terms of labour productivity even when accounting for management score and other observed characteristics. This indicates that family-owned businesses may have different characteristics compared to non-family-owned ones, which are not captured in our data. We are mindful that our sample is relatively small, and are developing a follow on survey using a larger sample to support more robust conclusions. Further work in this area would also involve exploring opportunities to compare results of the MPS with similar surveys from other countries, such as the German Management and Organisational Practices Survey and the US Management and Organizational survey.

2. ONS, 2017, “International comparisons of UK productivity (ICP), final estimates: 2015” www.ons.gov.uk/economy/economicoutputandproductivity/ productivitymeasures/bulletins/internationalcomparisonsofproductivityfinalest imates/2015 [Accessed online: 5 April 2017] 3.These questions were inspired by and broadly consistent with the Management and Organizational Practices Survey (MOPS) created by the U.S. Census Bureau. 4. Awano, G. & Robinson, H. 2016, “Experimental data on the management practices of manufacturing businesses in Great Britain: 2016”, Office for National Statistics, www.ons.gov.uk/employmentandlabourmarket/peo pleinwork/labourproductivity/articles/experimentaldataonthemanagement practicesofmanufacturingbusinessesingreatbritain/2016 5. Awano, G., Heffernan, A. & Robinson, H. 2017, “Management practices and productivity among manufacturing businesses in Great Britain: Experimental estimates for 2015”, Office for National Statistics, www.ons.gov. uk/employmentandlabourmarket/peopleinwork/labourproductivity/articles/ experimentaldataonthemanagementpracticesofmanufacturingbusinessesi ngreatbritain/experimentalestimatesfor2015 6. The median management score is 0.59 and the score for the 75th percentile is 0.72.

1. Bloom, Nicholas, Renata Lemos, Raffaella Sadun, Daniela Scur & John Van Reenen, 2014, “The New Empirical Economics of Management”, NBER Working Paper No. 20102, www.nber.org/papers/w20102 [Accessed online: 17 March 2017]

OECD Supply and Use Tables Datasets available for the first time The OECD Supply and Use Tables (SUT) datasets are now available on OECD.Stat (www.oecd.org/std/na/supply-and-use-tables-database.htm) as part of the wider set of information on National Accounts statistics. The tables provide information by industry (at the 2 digit ISIC Rev 4 level: 89 industries) with corresponding breakdowns by product (using the comparable European CPA product breakdown). They are available at the total economy level, with separate splits of domestic and import use, at both purchasers and basic prices, and contain complementary tables describing the differences between the two price basis broken down by trade and transport margins and taxes and subsidies on products. The database currently covers 37 countries for a range of years, varying by country, going from 1997 to 2015. Additional countries and years will be added on an on-going basis. Read our latest Statistical Insights: What role for Supply and Use Tables for further information: http://bit.ly/statinsights-SUT.

Issue No. 66, June 2017 - The OECD Statistics Newsletter 7

How to measure global statistical literacy? Thilo Klein, PARIS21, OECD Statistics Directorate (PARIS21@oecd.org)

PARIS21 has carried out automated text analysis of articles published on the RSS feeds of the top five n his Synthesis Report on the Post-2015 Agenda national newspapers in 122 countries using the open The Road to Dignity by 2030 (United Nations, 2015), source statistical software R (R Core Team, 2017) every former UN Secretary-General Ban Ki-moon called day since 15 April 2016. Articles are classified into three for a transformative agenda and stressed that “the levels of literacy based on a list of keyword sequences. world must acquire a new data literacy in order to be The three levels are derived from the statistical literacy equipped with the tools, methodologies, capacities, and construct defined in Watson and Callingham (2003) who information necessary to shine a light on the challenges build on the definition of statistical literacy as “the ability of responding to the new agenda”. But what level of data to critically evaluate statistical material and to appreciate literacy is required, and how close are we to achieving it? the relevance of statistically-based approaches to all aspects of life in general” (Oxford Dictionary of Statistical To shed light on this question, the Terms, Dodge, 2003). The three levels The world must OECD-hosted Partnership in Statistics of statistical literacy used in the analysis acquire a new for Development in the 21st Century are: (1) consistent, non-critical use of data literacy (...) (PARIS21) has set up a Task Team statistics; (2) critical use of statistics; and composed of representatives of National to shine a light on (3) critical mathematical engagement with Statistical Offices, international and the challenges of statistics (see Table 1). regional organisations (including Afristat, responding to the A level 1 article might make reference to OECD, UNESCAP and World Bank), development agencies, statistical training Post-2015 Agenda. key sources of statistical data, such as a centres and statistical associations. In population survey or household survey. order to develop a benchmark that nations can use It may also reference indicators like gross domestic to chart their progress, the Task Team has chosen product or consumer price indices. Level 2 articles newspaper coverage of statistics as a proxy measure might include adjectives that indicate that the accuracy, of statistical literacy. reliability and validity of data are being evaluated, while level 3 articles could include references to statistical The work of PARIS21 starts with the premise that a biases and fallacies, or to concepts like “measurement nation's demand for (and consumption of) statistical error”. Articles can, and typically do, fall into more than facts and depth of critical analysis can be reflected in one category. what journalists write. Newspaper articles are generally available, making them a good data source for a country's Why not use more direct measures? literate population. To measure statistical literacy A limitation of using more direct measures based on empirically, PARIS21 has chosen to look at references surveys or skills assessments is that they often cover to statistics and statistical fallacies in online articles as broader concepts for a smaller population, are reported they are easily accessible, frequently reported and can infrequently and/or are not comparable across countries. be compared across countries. Alternative measures, such as the percentage of a What is being measured?

Table 1. PARIS21’s statistical literacy construct, adapted from Watson and Callingham (2003) by Klein et al. (2016) Levels

Brief characterisation of levels

1. Consistent non-critical

Appropriate but non-critical engagement with context, multiple aspects of terminology usage

2. Critical

Critical, questioning engagement in contexts that do not involve proportional reasoning, but which do involve appropriate use of terminology

3. Critical mathematical

Critical, questioning engagement with context, using proportional reasoning particularly in chance contexts, showing appreciation of the need for uncertainty in making predictions, and interpreting subtle aspects of language

8 The OECD Statistics Newsletter - Issue No. 66, June 2017

population working in job categories related to statistics (based on survey data from the Demographic and Health Surveys (DHS)) or literacy rates based on the World Bank’s Global dataset on education achievement (Angrist et al., 2013) are discussed in Klein et al. (2016).

Figure 1. Correlation between Statistical literacy scores and TIMSS results on the “Data and Chance” section for the year 2011 with 95% confidence interval and multiple R-squared of 0.17

Relation to international literacy assessments The literacy scores obtained from the approach above for 21 developed countries and US states have been correlated against another available proxy, the Trends in International Mathematics and Science Study (TIMSS), to confirm the external validity of the indicator. TIMSS is an international assessment of student mathematical achievement, evaluating at least 5,000 students per participating educational system. The TIMSS section on “Data and Chance” aims to capture some concepts that are very similar to the PARIS21 indicator. The 2011 TIMSS results correlate well (significant at the 7%-level) with our literacy score for a sample of 21 developed countries and US states (see Figure 1) for which both metrics are available. What are the results? Figure 2 shows the breakdown of scores by countries according to income groups based on the DAC classification (OECD, 2014). The score is simply the sum of percentages over the three literacy levels and is scored out of 300 (100 for each level). The breakdown includes the UK as a reference as the top scoring country of the four OECD countries (France, Mexico, Portugal and UK) used to represent the four languages of the

analysis (French, Spanish, Portuguese and English). The UK has an overall score of 22.93. Two seemingly unexpected results are worth noting. First, the average score of Lower Middle Income Countries (LMIC) at 20.31 exceeds that of Upper Middle Income Countries (UMIC) at 18.12. Second, there are a few countries in the UMIC and LMIC groups for which the literacy score exceeds that of the UK and other OECD countries. The following may provide some explanations for these findings. There are countries that perform particularly well in the LMIC group (such as the Philippines with a score of 28.57) and in the UMIC group (such as Mexico with 24.77). These results may be explained by strong statistical institutes in Mexico (INEGI) and the Philippines (PSA) that are very engaged in monitoring the use of statistics by journalists. The PSA, for example, tracks references to their institutions via Google news subscriptions and engages with the media. INEGI reports on the impact and value of statistics based on daily monitoring of newspapers and media resources (UNECE, 2017). In addition to the above, one also needs to be aware of a number of limitations of the methodology that can explain differences between countries, including:

Figure 2. Distribution of statistical literacy scores for the UK compared to DAC groups Average Scores of Statistical Literacy

United Kingdom

22.93

Upper Middle Income Countries

18.12

Lower Middle Income Countries

20.31

Least Developed Countries

14.75 0

•• Top five newspapers by country have different audiences. In the UK, tabloids are included in the top five national newspapers, which may explain a lower score. In developing countries, as the current analysis is limited to four of the most widely spoken languages globally, it does not include local languages in these countries, the local Tagalog

Issue No. 66, June 2017 - The OECD Statistics Newsletter 9

Figure 3. World map showing the current geographical coverage of PARIS21’s automated text analysis of statistical content in newspapers for 33 IDA countries as well as France, Mexico, Portugal and the UK Countries are coloured according to statistical literacy score (out of 300)

press in the Philippines being a case in point. Readers of English-language newspapers in the Philippines might have different levels of education, income and interest in statistics than the general population. •• Newspapers and blogs are only a subset of national media. Radio and TV cannot easily be captured in a machine readable format. New promising tools, such as the Radio Analysis tools developed by UN Global Pulse Lab Kampala (2017) and the United Nations in Uganda, could fill this gap in the coming years. •• Literacy would need to be tested against the “appropriateness” of terms used. The indicator measures a count of terms, whereas literacy would ideally also need to be tested against the “appropriateness” of the terms used, in context. Way forward The limitations of the current approach serve to highlight the fundamental difficulty of measuring statistical literacy in a way that is comparable internationally – a challenge faced by all those working towards the UN’s many and varied Sustainable Development Goals. World leaders may have decided that “data literacy” is important but have left it to the international community to give meaning to this.

10 The OECD Statistics Newsletter - Issue No. 66, June 2017

PARIS21’s literacy indicator is a first step in this direction. It currently covers news articles written in four languages for 33 of the 77 International Development Association (IDA) borrowing countries (see Figure 3). In total, the data collection is currently undertaken for 122 countries, and the baseline results for these countries are made available on the PARIS21 website at www.paris21.org/ literacy as more articles become available and the country scores stabilise. The indicator has also been reported as part of the Busan Action Plan for Statistics (PARIS21, 2011) logical framework, which was agreed at the 4th High Level Forum on Aid Effectiveness in 2011.

References Angrist, N., H.A. Patrinos and M. Schlotter (2013). An Expansion of a Global Data Set on Educational Quality: A Focus on Achievement in Developing Countries, Policy Research Working Paper Series 6536, The World Bank. Dodge, Y. (2003). Oxford Dictionary of Statistical Terms, Oxford University Press. Klein, T., Galdin, A. and Mohamedou, E. (2016) An indicator for statistical literacy based on national

newspaper archives. In Proceedings of the Roundtable Conference of the International Association of Statistics Education (IASE), Berlin, Germany. http://bit.ly/2ibU4kr OECD (2014). DAC List of ODA Recipients. OECD Development Assistance Committee. Accessed at www. oecd.org/dac/stats/documentupload/DAC%20List%20 of%20ODA%20Recipients%202014%20final.pdf PARIS21 (2011). Statistics for Transparency, Accountability, and Results: A Busan Action Plan for Statistics. Accessed at www.paris21.org/sites/default/ files/Busanactionplan_nov2011.pdf PARIS21 (2017). An indicator for statistical literacy based on national newspaper archives, Website. Accessed at www.paris21.org/literacy R Core Team (2017). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. United Nations (2015). The Road to Dignity by 2030, Synthesis Report of the UN Secretary-General on the Post-2015 Agenda. UNECE (2017). Value of official statistics: Recommendations on promoting, measuring and communicating the value of official statistics. Accessed at www.unece.org/statistics/statstos/task-force-on-thevalue-of-official-statistics.html UN Global Pulse Lab Kampala (2017). Analysing radio content (website). Accessed at http://radio.unglobalpulse. net/uganda/ Watson, J. M. and Callingham, R. A. (2003) Statistical literacy: A complex hierarchical construct. Statistics Education Research Journal, 2(2), 3–46.

Measuring the impact of business on well-being Well-being as a concept emphasises people and society’s opportunities to thrive in a wide range of dimensions that matter most to people’s life, including not only economic living conditions but also quality of life and sustainability. The OECD framework for measuring countries’ well-being focuses on outcomes pertaining to individuals and households and their distributions. Businesses have a significant impact on people’s lives and society at large, through direct employment, supply chains, as consumers or investors. Better understanding the main aspects of well-being can also help improve business performance by gaining a deeper understanding of drivers in the market. Although businesses face a growing demand to report on their performance and impacts, by consumers, civil society or regulators, and a wide range of tools and frameworks for reporting, there is a considerable gap in conventionally accepted measurement frameworks that capture well-being. The OECD is expanding its work on measuring wellbeing at the country level to include the business community. On 23-24 February 2017, a workshop on measuring business impacts on people’s well-being was organised, representing a first milestone for the Measuring Business Impacts on OECD work with the business People’s Well-Being community on well-being. A wide range of stakeholders Highlights from the business community, civil society and academia came together to share their knowledge. Highlights are available at: http://oe.cd/ Biz4WB. 23-24 February 2017

#Biz4WB http://oe.cd/Biz4WB

To develop and support research on measuring the impact of business on well-being, the OECD Statistics Directorate, along with HEC’s Society and Organizations Center, are issuing a call for papers in the coming weeks. The focus will be on showcasing best practices, investigating the impact of reporting on well-being on businesses and society, identifying commonly used metrics and other relevant issues. Selected papers will be presented at the 6th OECD World Forum on Statistics, Knowledge and Policy in Korea in November 2018. Look for the call for papers at: http://oe.cd/Biz4WB.

Issue No. 66, June 2017 - The OECD Statistics Newsletter 11

OECD Compendium of Productivity Indicators OECD Compendium of Productivity Indicators 2017 This report presents a comprehensive overview of recent and longer-term trends in productivity levels and growth in OECD countries, accession countries, key partners and G20 countries. It includes measures of labour productivity, capital productivity and multifactor productivity, as well as indicators of international competitiveness.

2017 edition recently launched

This yearâ&#x20AC;&#x2122;s edition includes a special chapter on the relationship between productivity growth and growth in real wages, with particular focus on the post-crisis period. The chapter explores the challenges of measuring the wage-productivity gap and labour income shares. Consult this publication on line at http://dx.doi.org/10.1787/pdtvy-2017-en.

This work is published on the OECD iLibrary, which gathers all OECD books, periodicals and statistical databases. Visit www.oecd-ilibrary.org for more information.

ISBN 978-92-64-27325-2 30 2017 02 1 P

9HSTCQE*chdcfc+ Key findings for the post-crisis period include evidence on an increasing contribution of labour utilisation to GDP per capita growth, a slowdown in capital deepening, and lower differentials between investment price inflation and wage inflation in many countries.

Productivity by firm size

Source: OECD (2017), OECD Compendium of Productivity Indicators 2017, OECD Publishing, Paris. http://dx.doi.org/10.1787/pdtvy-2017-en

The OECD Statistics Newsletter - Issue No. 66, June 2017

OECD Compendium of Productivity Indicators 2017

The 2017 edition of the OECD Compendium of Productivity Indicators (http://oe.cd/productivity-compendium), released on 18 May, presents a comprehensive overview on recent and longer-term trends in productivity in OECD countries, accession countries and key partner economies.

OECD Compendium of Productivity Indicators 2017

EIGE’s gender statistics database European Institute for Gender Equality (EIGE)1 (eige.sec@eige.europa.eu)

ender statistics are used for understanding the roles and realities of women and men in society, the economy and/or the family and develop targeted gender policies. It is an area that intersects with traditional fields of statistics, as statistics are disaggregated by sex and then analysed from a gender perspective that takes into account the different challenges experienced by women and men. As recommended in the Beijing Platform for Action2, the coverage of gender concerns by statistical systems and the adequacy of such systems should be regularly reviewed, updated and improved. The review should check if relevant gender concerns are covered by existing data collection programmes and if they are publicly available. The European Institute for Gender Equality’s (EIGE) Gender Statistics Database contributes to fill a gap as data are not always easily accessible and are not presented in a comparative perspective (between countries and also over time). A user-friendly interface was designed by EIGE for both experienced statisticians and non-expert users. The content and functionalities allow the user to search and explore gender statistics using a keyword search or by choosing from a set of 6 predefined entry points. Following the calendar release from different data sources, new data are uploaded automatically to ensure that the most up-to-date information is always accessible.

Which data sources are used? The database contains, harmonised at the EU level, internationally comparable data from sources such as Eurostat, Eurofound and the European Union Agency for Fundamental Rights (FRA). A number of national sources are also included. All data are made publicly available at macro level (at Member State and EU levels). However, some of the macro data included in the database is computed by EIGE from micro data (data at the individual or household level).3

How often is it updated? EIGE’s Gender Statistics Database is updated on a rolling basis as soon as new data becomes available, this process is automated whenever possible, which ensures timely and error-free updating of data and metadata.

Women and men in decision-making Recently, EIGE has taken over the database on women and men in decision-making area (http://eige.europa. eu/gender-statistics/dgs/browse/wmidm) which was

The tree structure of the Database groups data views into several entry points which follow general areas of interest, particularly, the framework of European Union policy priorities. One of these entry points reflects EIGE’s commitment to ensuring the storage, collection and dissemination of data in the area of women and men in decision-making.

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previously managed by the European Commission’s DG Justice, Consumers and Gender equality. What areas of decision-making are covered? Data on decision-making are collected for the 28 EU Member States and seven other European countries.4 The domains covered include: • • • •

politics public administration judiciary business and finance

The OECD Statistics Newsletter - Issue No. 66, June 2017

• social partners and NGOs • environment and climate change • media Figures are available at international, European, national, regional and local level. Most data are updated annually, but some key data are updated more frequently. In particular, data on national and European political decision-making are updated quarterly to ensure that the information is always up-to-date. The data on the gender balance in key decision-making positions in large listed companies are collected every six months.

What is a decision-making position? In EIGEâ&#x20AC;&#x2122;s Gender Statistics Database, a decision-making position is defined as a position from which it is possible to take or influence a decision. It is measured according to two different criteria: 1. Within a domain: at organisational level. This refers to organisations with major influence in a specific domain at national, international, European, regional or local levels.

often appointed to portfolios such as education, health and culture, reinforcing traditional stereotypes about womenâ&#x20AC;&#x2122;s roles and expertise. Their male counterparts tend to be designated to more hard-line and traditionally masculine areas such as defence, security and technology.

In the private sector, the progress towards gender parity is mixed, with some areas advancing faster than others. There has been an upward trend in the In national number of women in boardrooms since 2. Within an organisation: at hierarchical parliaments, women 2010, when the matter became a priority level. This refers to top positions within on the European Commission's political account for less an organisation that are considered to agenda. The proportion of women on the than a third of the boards of the largest listed companies have a major input in decision-making. members. in the EU doubled from 11.9% in 2010 to 23.9% in 2016. Nevertheless, at the Some key figures most senior levels of top management, only 5.7% of The number of women holding decision-making positions CEO positions are held by women. has been gradually increasing over the last ten years, from politics to business and media. The latest figures Gender imbalance is also common across the EU media on women and men in decision-making show that the landscape. Although nearly two thirds of graduates from EU is taking a slow but steady path towards genderjournalism courses are women, few tend to advance to balanced representation. senior posts compared to men. Only a third of decisionmaking positions in this field across the EU are held by In national parliaments, women account for less than women. A positive trend can be noted among public a third of the members. This figure varies considerably broadcasting organisations, where the percentage of across EU countries, from 46.1% in Sweden to 9.5% in women holding board seats went up from 30.4% in 2014 Hungary. Gender imbalance is further reflected in the to 35% in 2016. division of ministersâ&#x20AC;&#x2122; portfolios. Women politicians are

Issue No. 66, June 2017 - The OECD Statistics Newsletter

Working better together EIGE’s work on its gender statistics database aims to be a reliable resource in the formulation and monitoring of policies that are beneficial for both women and men in EU countries. Through its database, EIGE continuously strives to serve the interests and needs of its stakeholders by gathering feedback and promoting a strong cooperation and collaboration between users and producers of gender statistics. The collaboration entails sharing of knowledge, data, methodologies, services and resources where appropriate. The database is a communication tool and therefore needs to be regularly reviewed, updated and adjusted in order to stay relevant and meet the needs of new users. Creating synergies and complementarities with key strategic stakeholders at national and international level, including those with similar initiatives on gender statistics, is of paramount importance.

For more information on the situation of women and men in decision-making, read our report: h t t p: //e i g e.e u r o p a .e u /r d c /e i g e - p u b l i c a t i o n s / gender-equality-power-and-decision-making-report

1. The European Institute for Gender Equality (EIGE) is an autonomous body of the European Union, established to contribute to and strengthen the promotion of gender equality, including gender mainstreaming in all EU policies and the resulting national policies, and the fight against discrimination based on sex, as well as to raise EU citizens’ awareness of gender equality. 2. Paragraph 207 (b) of the Beijing Platform for Action (United Nations, 1996)

Access EIGE’s Gender Statistics Database at http:// eige.europa.eu/gender-statistics/dgs

3. Statistics macro data refers to the result of a statistical transformation process in the form of aggregated information (Eurostat, RAMON — Reference and Management of Nomenclatures). Statistics micro data refers to non-aggregated observations, or measurements of characteristics of individual units (Eurostat, RAMON — Reference and Management of Nomenclatures).

Access Database on women and men in decisionmaking at http://eige.europa.eu/gender-statistics/dgs/ browse/wmidm

4. Four candidate countries (Montenegro, the Former Yugoslav Republic of Macedonia, Serbia and Turkey) and the remaining EEA countries (Iceland, Liechtenstein and Norway).

OECD Statistical Insights Indicators Key findings Data Evidence Method

Interested in the story behind the indicator? Check out the OECD Statistical Insights blog series (http://oecdinsights. org/category/statinsights) that focuses on indicators that are less visible than standard headline indicators, but that provide interesting evidence for analysis and policy making in some areas. The latest stories include: • What role for Supply-Use Tables? • Large inequalities in longevity by gender and education in OECD countries • Job strain affects four out of ten European workers • Inclusive Globalisation, does firm size matter? • Blowing bubbles? Developments in house prices

The OECD Statistics Newsletter - Issue No. 66, June 2017

Generating good practices for the follow up to the 2030 Agenda: the Colombian experience Mauricio Perfetti, Chief Statistician, National Administrative Department of Statistics of Colombia (mperfetti@dane.gov.co)

he 2030 Agenda and the Sustainable Development Goals (SDGs) undoubtedly represent great challenges for countries, but has also served as a catalyst to search for new strategies and practices for achieving these goals, as well as the design of innovative and collaborative institutional instruments for a proper follow-up. Presidential Commitment Colombia's participation in the definition of the 2030 Agenda since 2012 has enabled the country to anticipate and think more broadly about the organisational structures, mechanisms and national planning needed for effective implementation and follow-up. The national commitment for the follow-up of SDGs began in 2015 with the creation of the High-level InterInstitutional Commission by the Presidency of the Republic. The aim was to facilitate inter-institutional dialogue and coordination, optimising available

resources. The National Administrative Department of Statistics of Colombia (DANE) is part of this Commission and leads the Working Group on SDG Indicators within the framework. Global and regional coordination: Leadership planning the follow-up Colombia is part of the Inter-Agency and Expert Group on Sustainable Development Goal Indicators (IAEG-SDG). As a representative of the sub-region of Andean countries, in 2015, DANE formally created a Working Group dedicated exclusively to SDGs to foster interactions between the different entities within countries, across Andean countries and also with other countries in Latin America and the Caribbean. During 2015, 20 workshops were carried out with entities at the national level in order to assess the feasibility, relevance and suitability of each of the proposed global indicators. The exercise was replicated with the

Source: National Planning Department (DNP)

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Source: DANE

other Andean countries, consolidating the positions of Colombia and the sub-region. At the regional level, in June 2016, within the framework of the Statistical Conference of the Americas (SCA), the Statistical Coordination Group for the 2030 Agenda in Latin America and the Caribbean was created, and included regional representatives from IAEG-SDG (Colombia, Brazil, Mexico, Jamaica and Cuba) and from the High Level Group (HLG) (Ecuador, Argentina, El Salvador, Bahamas and St. Lucia). This Group aims to coordinate the elaboration and implementation process of regional indicators and the development of capacities. The group will submit an annual report to the Executive Committee of SCA of ECLAC, which in turn will present these results at, and seek feedback and approval from the SCA. The creation of institutional coordination mechanisms between national entities and countries is thus a good practice favouring effective monitoring of the 2030 Agenda. Implementation of the global indicator framework: Diagnosis and identification of needs One of the main results of the work carried out by the IAEG-SDG is the development of a set of 231 global indicators to monitor the 169 targets of the SDGs.

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As a starting point, DANE together with other entities of the National Statistical System (NSS) of Colombia carried out an assessment of available information needed in Colombia. On the basis of this exercise, a control board was created (shown on page 19), that identifies the status of the indicators for each target. The figure below shows that 54% of indicators can be classified as green (i.e. for which information is available); 30% in yellow (i.e. partial information is available or indicators require improvements); and 16% in red (i.e. information is not available or a methodology has not yet been defined to collect the information). This diagnosis has been the basis for progress in the definition of work plans through different strategies, within the framework of the Working Group on Indicators of the Inter-Institutional Commission. At the UN level, in 2016 a global subgroup was created between the IAEG-SDG and the HLG that was tasked with developing a plan to address the immediate priorities for implementing the SDG indicators. Colombia as part of this subgroup has promoted regional evaluations, proposing as an example the experience of Latin America and the Caribbean. Specifically, through the Statistical Coordination Group for the 2030 Agenda, a Questionnaire of Statistical Capacity for the production of SDG indicators was designed and implemented as a first step to determine the current status of the region and to develop cooperation strategies for closing gaps.

GOALS

Among the main challenges identified in the region through these diagnoses, the following stand out:

1 2 3 4

• Measurement of new thematic areas.

5 6 7

• Generation of strategies to fill the information gaps identified.

9 10

TARGETS

• Disaggregation of information required to 'leave no one behind'.

11 12 13

• Strengthening of statistical capacities at both the national and the territorial levels.

• Strengthening of partnerships with different actors such as private sector, civil society, academia, etc.

• Mobilisation of technical and financial cooperation.

15 16 17

19 A B C D

Source: DANE

• Strengthening of the role of the NSOs as coordinators of the NSS. National ownership: framework of national indicators For national ownership of the 2030 Agenda, national monitoring is essential, as it ensures transparency and inclusion, and supports a deeper and more effective implementation. In this sense, Colombia has made progress in defining its national monitoring framework for SDGs through the Inter-institutional Commission with the following actions: i) the development by the Indicators Working Group led by DANE of 15 workshops per thematic area, where 60 entities of the NSS participated, to jointly define the national indicators that are the basis for setting the specific targets of Colombia by 2030; and ii) working on the release of a national policy document that seeks to incorporate SDG targets in the country's long-term planning and to define the monitoring and reporting scheme of the Agenda.

Available Information: 54% Partial Information, need of improvements: 30% Not data or not methodology: 16%

• Constructing the National Statistical Plan, prioritising information needed for SDGs indicators to ensure adequate resources. • Territorial work with the support of private sector organisations in 8 cities to raise awareness of different sectors on the need to generate timely and quality information. • Working Group with agencies of the UN System in order to collaborate in 4 areas: i) Information gaps; ii) Promotion of territorial statistics; iii) Promotion of

Strengthening capacities through partnerships In order to overcome the challenges identified with respect to the monitoring of SDGs it is necessary to work hand-in-hand with different actors. Colombia has defined lines of action to make progress on the implementation of monitoring frameworks through various alliances:

Source: DANE

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partnerships; and, iv) Dissemination and socialisation actions. • Within the framework of this Working Group, DANE led the First Andean Congress on Data for SDGs, held in Bogotá in March 2017. Through 5 thematic blocks aligned with the 5 critical areas of the 2030 Agenda (Partnerships, People, Prosperity, Planet and Peace), work plans were established for the measurement of 31 global indicators that represent a challenge for the sub-region. 240 people from 47 institutions, including statistical offices, government agencies, UN agencies, academia and civil society organisations participated. • Working jointly with the Centre for International Strategic Thinking (CEPEI) and the Global Partnership for Sustainable Development Data (GPSDD), to identify potential alternative sources of information and to take advantage of the data revolution. Implementing innovative mechanisms to fill gaps Colombia has also made progress towards including innovative strategies to fill SDGs data gaps. In 2016, in DANE, through the Innovation and Learning Contest, projects were launched to fill the information gaps using new sources and methodologies. Some of these projects aim to combine different sources of information, such as the National Agriculture and Livestock Census, population projections, cartographic information, satellite images and land use planning, among others, with the purpose of addressing gaps

related to rural road infrastructure, consumption of urban land and access to public spaces (Global Indicators 9.1.1, 11.3.1 and 11.7.1). Others aim to incorporate innovations in information capture methods, for example using drones to monitor changes in ecosystems. Under the GPSDD, Colombia has received support from experts for the measurement of global indicator 11.3.1 (Ratio of land consumption rate to population growth rate) through the use of satellite images. The methodology, which can be readily applied in other countries, has already been carried out for 6 metropolitan areas and is now being conducted in 151 cities. Under the same framework, a workshop was held in Bogotá with the participation of the National Aeronautics and Space Administration's Group on Earth Observations (NASA, GEO), the University of Maryland, the Committee on Earth Observation Satellites (CEOS) and the European Space Agency (ESA), the Ministry of Environment, the Institute of Hydrology, Meteorology and Environmental Studies (IDEAM1) and DANE, in order to define cooperation and work plans for the measurement of other indicators that require the use of high-quality satellite images as well as processing capacity. Conclusions The 2030 Agenda represents a significant measurement challenge, which has encouraged the creation of interinstitutional mechanisms at the local and regional levels. Colombia, in particular, has made progress in diverse monitoring mechanisms, addressing medium and long-term planning processes, monitoring at the national level, as well as coordinating statistical agencies in the region to get the support of different actors to develop the SDG indicators. Furthermore, it has sought innovative and collaborative mechanisms, generating strategic partnerships with private organisations, academia and international organisations that have allowed more timely responses. These good practices can hopefully help other countries establish their own route towards achieving the SDGs.

Source: DANE

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1. For its acronym in Spanish

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

OECD Date

Meeting

20 June 2017

Working Party on Industry Analysis (WPIA)/Intellectual Property Statistics Task Force, Directorate for Science, Technology and Innovation. OECD, Paris, France 20-21 June 2017 DAC Working Party on Development Finance Statistics, Formal Meeting. Development Co-operation Directorate. OECD, Paris, France 21-22 June 2017 14th meeting of the OECD Committee on Statistics and Statistical Policy, Statistics Directorate. Geneva, Switzerland 22 June 2017 Working Party on Industry Analysis (WPIA)/Working Group on Statistics, Directorate for Science, Technology and Innovation. OECD, Paris, France 22-23 June 2017 OECD Space Forum Workshop on Economic Indicators for the Space Sector, Directorate for Science, Technology and Innovation. OECD, Paris, France 26-27 June 2017 2017 Conference of the Global Forum on Productivity, Budapest, Hungary www.oecd.org/global-forum-productivity/events/global-forum-on-productivity-budapest-2017.htm 27-29 September 2017 Eurostat-OECD-UNECE meeting of Business Register Experts, Statistics Directorate. OECD, Paris, France 2-3 October Expert meeting on Statistical Data and Metadata Exchange, Statistics Directorate. Addis Ababa, Ethiopia 2-3 October 2017 OECD-UNECE Seminar on the Implementation of the System of Environmental-Economic Accounting, UNECE. Geneva, Switzerland 4-6 October 2017 SOPEMI expert meeting on migration, Directorate for Employment, Labour and Social Affairs. OECD, Paris, France 10-11 October 2017 OECD-WTO Task Force on International Trade Statistics, Statistics Directorate. OECD, Paris, France 11 October 2017 17th International Economic Forum on Africa, Development Centre. OECD, Paris, France 11-12 October 2017 18 October 2017 17-19 October 2017 6-10 November 2017 7-9 November 2017 16-17 November 2017

4-5 December 2017 5 December 2017 11-15 December 2017

OECD-WTO Task Force on International Trade Statistics, Expert Group Meeting on Measuring Digital Trade, Statistics Directorate. OECD, Paris, France 15th meeting of the G20/OECD Task Force on Institutional Investors and Long-term Financing, Directorate for Financial and Enterprise Affairs. OECD, Paris, France Working Group on International Investment, Directorate for Financial and Enterprise Affairs. OECD, Paris, France Working Party on Financial Statistics and on National Accounts, Statistics Directorate. OECD, Paris, France Working Party No. 2 on Tax Policy Analysis and Tax Statistics, Centre for Tax Policy and Administration. OECD, Paris, France 8th joint EC/OECD Workshop on recent developments in Business & Consumer Surveys, Statistics Directorate. Brussels, Belgium www.oecd.org/std/leading-indicators/bcs2017.htm Advisory Expert Group (AEG) on National Accounts, Statistics Directorate. New York, United States Working Party on Territorial Indicators, Directorate for Public Governance. OECD, Paris, France Working Party of National Experts on Science and Technology Indicators (NESTI), Directorate for Science, Technology and Innovation. OECD, Paris, France

Other meetings 12-16 June 2017 19-21 June 2017

World Summit on the Information Society Forum, Geneva, Switzerland www.itu.int/net4/wsis/forum/2017/ UNECE Conference of European Statisticians, UNECE. Geneva, Switzerland

7-8 July 2017

G20 Summit, Hamburg, Germany

13-15 October 2017

2017 Annual Meetings of the World Bank Group and the International Monetary Fund. Washington, D.C., United States

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Recent publications OECD Finance and Business Outlook 2017 Globalisation has failed to create a level playing field in trade, investment and corporate behaviour, being one of the factors contributing to a backlash against openness in many countries and a decline in confidence in government institutions. Only by stepping up international co-operation and improving and ensuring global markets, companies and institutions play according to the same rule book can productivity growth be restored, excess capacity avoided and public confidence improved, according to a new OECD report. The OECD Business and Finance Outlook 2017 says that strengthening global governance and co-operation on corporate and financial issues involves establishing “rules of the game” which are both fair and perceived by all to be fair. OECD (2017), OECD Business and Finance Outlook 2017, OECD Publishing, Paris. www.oecd.org/daf/oecd-business-and-finance-outlook-2017-9789264274891-en.htm

African Economic Outlook 2017 Entrepreneurship and Industrialisation The African Economic Outlook 2017 presents the continent’s current state of affairs and forecasts its situation for the coming two years. This annual report examines Africa’s performance in crucial areas: macroeconomics, external financial flows and tax revenues, trade policies and regional integration, human development, and governance. For its 16th edition, the report takes a hard look at the role of entrepreneurs in Africa’s industrialisation process. It proposes practical steps that African governments can take to carry out effective industrialisation strategies. Policies aimed at improving skills, business clusters and financing could remove important constraints on African private enterprises. AfDB/OECD/UNDP (2017), African Economic Outlook 2017: Entrepreneurship and Industrialisation, OECD Publishing, Paris. www.africaneconomicoutlook.org/en/home

Oil 2017 This year marks a new period of oil market management by leading oil producers, who put together in late 2016 the most comprehensive agreement to limit oil output seen since 2009. The reason was to ensure that oil prices were stabilised to avoid economic dislocation in producing countries and to provide a platform for gradual growth. The agreement brought to an end a two-year free market window in which producers competed to secure outlets for their oil. This agreement provides the backdrop to the latest IEA five-year oil market forecast, which was renamed Market Report Series: Oil 2017 (formerly known as the Medium-Term Oil Market Report). While we cannot know how long the deal will last, it provides clear trends to guide our view of the next five years. IEA (2017), Oil 2017, IEA, Paris. www.iea.org/bookshop/740-Market_Report_Series:_Oil_2017

22 The OECD Statistics Newsletter - Issue No. 66, June 2017

Recent publications Small, Medium, Strong. Trends in SME Performance and Business Conditions SMEs and entrepreneurs play a key role in national economies around the world, generating employment and income, contributing to innovation and knowledge diffusion, responding to new or niched demands and social needs, and enhancing social inclusion. However, SMEs are often more affected by business environment conditions and structural policies than larger firms. This report presents comparative evidence on SME performance and trends, and on a broad range of policy areas and business environment conditions that are important for small businesses. The analysis takes into account the multi-dimensionality of SME policy objectives and the significant heterogeneity of the SME population, within and across countries. Data and indicators on framework conditions are complemented with information on recent policy trends in OECD countries. OECD (2017), Small, Medium, Strong. Trends in SME Performance and Business Conditions, OECD Publishing, Paris. www.oecd.org/industry/small-medium-strong-trends-in-sme-performance-and-business-conditions9789264275683-en.htm OECD Skills Outlook 2017: Skills and Global Value Chains In an increasingly competitive international environment, providing workers with the right mix of skills can help ensure that globalisation translates into new jobs and productivity gains rather than negative economic and social outcomes, according to a new OECD report. The OECD Skills Outlook 2017 reveals big differences in the extent to which countries are equipping workers with the right skills to benefit from the globalisation of production chains. The report finds a country with a skills mix that is well aligned with the skills requirements of technologically advanced industries can specialise in these industries on average 8% more than other countries, and up to 60% more than countries with a low alignment between countries’ skills mix and these industries requirements. OECD (2017), OECD Skills Outlook 2017: Skills and Global Value Chains, OECD Publishing, Paris. www.oecd.org/edu/oecd-skills-outlook-2017-9789264273351-en.htm

Taxing Wages 2017 Special feature: Taxation and skills Taxes on labour income for the average worker across the OECD continued to decrease for the third consecutive year during 2016, dropping to 36% of labour costs, according to a new OECD report. Taxing Wages 2017 measures the level of personal income tax and social security contributions in each OECD country by calculating the ‘tax wedge’ - the total taxes on labour income paid by employees and employers, minus family benefits received, as a percentage of the labour costs of the employer. The tax wedge is calculated for a range of different family types and at different income levels. OECD (2017), Taxing Wages 2017, OECD Publishing, Paris. www.oecd.org/ctp/tax-policy/taxing-wages-20725124.htm

Issue No. 66, June 2017 - The OECD Statistics Newsletter 23

The Statistics Newsletter

for the extended OECD statistical network Issue 66 - June 2017 www.oecd.org/std/statisticsnewsletter To receive the OECD Statistics Newsletter by email, you can subscribe to OECDdirect e-mails: www.oecd.org/about/publishing/oecddirect.htm

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