The OECD Statistics Newsletter, Issue 74, July 2021

The Statistics Newsletter From the OECD s tatis tics and data communit y to the ex tended OECD s tatis tic al net work

FEATURING + Drawing on machine learning in the quest for effective teaching and learning + Distribution of household income, consumption and saving in line with national accounts + Improving the quality of foreign aid data through machine learning

THE LATEST APO-OECD REPORT: TOWARDS IMPROVED AND COMPARABLE PRODUCTIVITY STATISTICS oe.cd/statisticsnewsletter Issue No. 74, July 2021

Contents 3

Drawing on machine learning in the quest for effective teaching and learning

Gabor Fulop (gabor.fulop@oecd.org), Noémie Le Donné (noemie.ledonne@oecd.org), Directorate for Education and Skills, OECD

8

Distribution of household income, consumption and saving in line with national accounts

Jorrit Zwijnenburg (jorrit.zwijnenburg@oecd.org), Statistics and Data Directorate, OECD

13

Improving the quality of foreign aid data through machine learning

Shashwat Koirala (shashwat.koirala @oecd.org), Development Co-operation Directorate, Pedro Asti (pedro.asti@oecd.org) and Jan-Anno Schuur (Jan-Anno.schuur@oecd.org), Digital, Knowledge and Information Service, Executive Directorate, OECD

16

Recent publications

18

Forthcoming meetings

The Statistics Newsletter is published by the OECD Statistics and Data Directorate.

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@OECD_STAT

Editor-in-Chief: Paul Schreyer Editors: Ashley Ward and Graham Pilgrim Technical support: Sonia Primot Contact us at SDD.CommTeam@oecd.org

2 The OECD Statistics Newsletter - Issue No. 74, July 2021

Drawing on machine learning in the quest for effective teaching and learning Gabor Fulop (gabor.fulop@oecd.org), Noémie Le Donné (noemie.ledonne@oecd.org), Directorate for Education and Skills, OECD

W

ith most students having experienced remote learning over the past year due to COVID-19 related restrictions, the importance of teachers and schools has become only more evident.

Temporary school closures underline the significant benefits students receive from being in school with their teachers and classmates. But what exactly do teachers do that helps students perform well academically, socially and emotionally?

Identifying the teacher and school factors that help younger generations to succeed and thrive later in life is a longstanding challenge for education policy. Past education research has shown that how teachers, school leaders and schools shape the quality of instruction and students’ environment is closely related to student academic and social-emotional development (DarlingHammond, 2017[1]; OECD, 2018[2]).

Past studies have found that teachers’ value-added accounts for significant variation in student achievement (Chetty, Friedman and Rockoff, 2014[3]; Jackson, Rockoff and Staiger, 2014[4]; Rivkin, Hanushek and Kain, 2005[5]; Rockoff, 2004[6]). There is also evidence that, as with test scores, teachers vary considerably in their ability to support students’ social and emotional development (Jackson, 2018[7]; Kraft, 2019[8]; Ladd and Sorensen, 2017[9]).

The literature indicates that teachers and schools matter. However, the evidence is less conclusive as to the specific characteristics and actions of teachers and schools that matter the most for student achievement and social-emotional development. By applying a machine learning technique to a dataset that combines two large international surveys, the OECD report, Positive, Highachieving Students? What Schools and Teachers Can Do (OECD, 2021[10]), pinpoints some of the most effective teacher and school practices.

The TALIS-PISA link data The two surveys in question are the OECD Teaching and Learning International Survey (TALIS), which is

the largest international and periodic survey asking teachers and school leaders about their working conditions and learning environments, and the OECD Programme for International Student Assessment (PISA), which provides the most comprehensive and rigorous international assessment of student learning outcomes to date, delivering insights into the cognitive and socialemotional skills of 15-year-old students. We call this the TALIS-PISA link.

The TALIS-PISA link 2018 data comprises thousands of variables from more than 30,000 students and more than 15,000 teachers of the same schools from nine countries and sub-national entities: Australia, Ciudad Autónoma de Buenos Aires (referred to as CABA [Argentina]), Colombia, the Czech Republic, Denmark, Georgia, Malta, Turkey and Viet Nam. That being said, the specific survey design of the TALISPISA link data comes with its limitations. First, the data do not allow for matching a teacher and her or his students; rather, the data only permit matching a sample of teachers teaching 15-year-old students in a school and a sample of 15-year-old students at that same school.

Information on teachers is therefore averaged at the school level and then analysed together with students’ outcomes. Given that teachers of the same school differ significantly in terms of their characteristics and practices, linking data by averaging teachers’ variables at the school level constitutes a considerable loss of information. Second, the cross-sectional design of the TALIS and PISA studies prevents causal interpretation of the analyses based on the TALIS-PISA link data.

Drawing on a machine learning technique to let the data speak Applied education research has yet to tap into the rapidly expanding field of machine learning. Advanced datadriven methods are rarely applied in research looking at the nexus between teaching and learning. The latest OECD report analysing the TALIS-PISA link dataset (OECD, 2021[10]) seeks to break new ground by extracting maximum relevant information from this complex dataset.

Issue No. 74, July 2021 - The OECD Statistics Newsletter 3

A machine learning technique called “lasso” (i.e. least absolute shrinkage and selection operator) is used as a compass to guide the identification of the teacher and school characteristics and practices that matter the most for student outcomes. Lasso is an attractive tool for analysing data patterns emerging from the many variables collected through the TALIS survey and the many student outcomes measured by PISA. In particular, lasso can select variables that are highly correlated with the outcome variable even when the number of potential variables is high relative to the number of observations.

Lasso selects variables that correlate well with the outcome in one dataset (training sample) and then tests whether the selected variables predict the outcome well in another dataset (validation sample) (Hastie, Tibshirani and Friedman, 2017[11]). It proceeds with variable selection by minimising the prediction error of the regression

model subject to the constraint that the model is not too complex. Lasso reduces model complexity by omitting certain variables given the underlying assumption that the number of coefficients that are non-zero (therefore signalling a positive or negative association with the outcome variable) in the true model is small relative to the sample size (known as the sparsity assumption).

Theory and previous research findings are also carefully considered to inform the analyses and interpret, validate, or contextualise the findings. In addition, standard statistical methods complement the findings from lasso regressions. Variance decomposition techniques are applied to measure the share of variance in student outcomes explained by each of the high-level teacher and school dimensions considered in the analysis.

Standard linear and logistic regressions are then used country by country to determine the significance and

Figure 1. Teacher and school factors that matter for student academic success and social-emotional development

Effect on student achievement and socio-emotional development

Teacher

Level

School

Indirect

Direct

School dimensions with an indirect effect on student achievement:

School dimensions with a direct effect on student achievement:

- Induction - Mentoring - Professional development - Feedback - Collaboration - School innovativeness - Employment status - Formal appraisal

- Classroom characteristics - School culture

Teacher dimensions with an indirect effect on student achievement:

Teacher dimensions with a direct effect on student achievement:

- Teacher characteristics - Motivation to join the profession - Initial education and training - Well-being and job satisfaction - Self-efficacy - Use of working time

- Classroom practices

Student achievement by subject domain: - Reading - Mathematics - Science

Student social-emotional development: - Perception of classroom climate - Perception of teacher enthusiasm - Perception of difficulty of the PISA test - Educational expectations

Student characteristics associated with student achievement: - Gender - Immigrant background - Index of economic, social and cultural status

Notes: The relationships between teacher and school dimensions are often characterised by reciprocity and inter-connectedness. For example, professional development influences classroom practices, and in turn, those practices have an effect on the type of professional development provided to teachers.

Certain factors can be both an input and an output of schooling. Indeed, the reciprocity also holds for the relationship between various teacher and school factors, and student achievement. For instance, student performance can have an impact on the choice of teaching strategies applied in the classroom, but it can also influence other factors such as school culture (e.g. teacher-student relations), the type of professional development provided to teachers or teacher well-being, job satisfaction and self-efficacy.

Source: OECD (2021), Positive, High-achieving Students?: What Schools and Teachers Can Do, TALIS, OECD Publishing, Paris, https://doi.org/10.1787/3b9551db-en.

4 The OECD Statistics Newsletter - Issue No. 74, July 2021

sense of the relationships between teacher and school factors, and student outcomes.

Teacher and school factors that matter the most for student academic success and social-emotional development.

The report tests the potential influence of many teacher and school characteristics and practices – almost 150 variables grouped into 18 high-level dimensions – from teachers’ initial teacher education, motivations to join the profession, opportunities for collaboration through to classroom composition and teaching practices (Figure 1). As a result, it highlights five key predictors of student academic achievement and socialemotional development: teachers’ classroom practices (in particular, the share of class time spent on actual teaching and learning), teachers’ use of working time (in particular, the amount of time teachers spend on marking and correcting and on extracurricular activities), teachers’ well-being and job satisfaction, classmates’ characteristics and school culture (in particular, the involvement of parents and community in school-related activities as well as teacher-student relations).

More specifically, it is the time teachers spend actually teaching in class, not disciplining or taking care of administrative work, and the hours they spend marking

and correcting work, and going over this feedback with their students that links to how well students do academically, and how motivated and optimistic they are about their learning and prospects. Indeed, students tend to perform better, on average, the more class time teachers spend on actual teaching and learning.

Additionally, the more time teachers spend on marking and correcting student work, the better students perform academically (Figure 2) and the more likely they are to expect to complete at least a tertiary degree.

The report also suggests that positive teachers can help to form positive and high-achieving students. Indeed, students tend to find their teachers more interested in their teaching when teachers report lower levels of workrelated stress. Further to this, the more satisfied teachers are with their work environment, the better students tend to perform in school. Moreover, positive teacher-student relations are found to matter for student achievement and social-emotional development. In short, the more teachers report nurturing good relationships with students, the more students perceive them as enjoying teaching, the better it is for classroom disciplinary climate, and the better students perform academically Results also show that spending quality time with students outside of the usual lessons supports student growth. The more time teachers spend on extracurricular

Figure 2. Relationship between time spent by teachers on marking and correcting student work and student academic achievement Change in PISA score (points)

Reading

Mathematics

Science

20 15 10 5 0 -5

Georgia

Turkey

Czech Republic

Colombia

TALIS-PISA link average

Australia

Denmark

Malta

CABA (Argentina)

Notes: Teacher variables are averaged for all teachers within the school.

PISA scores are scaled to fit approximately normal distributions, with the OECD’s means around 500 score points and standard deviations around 100 score points.

Results of linear regression based on responses of 15-year-old students and teachers. Controlling for the following elements of teachers' use of working time: total working hours, total teaching hours and teachers' use of working time on tasks other than marking and correcting (such as individual planning or preparation of lessons either at school or out of school, or general administrative work); and for the following student characteristics: gender, immigrant background and index of economic, social and cultural status. TALIS-PISA link data are more likely to provide insights for the Czech Republic and Turkey, where differences in school average performances represent about half of the total variance in student achievements, as opposed to countries, including Australia, Denmark and Malta, where 25% or less of the total variation in student outcomes lie between schools. In addition, it is less likely to have statistically significant results for countries and economies with smaller sample sizes (e.g. Ciudad Autónoma de Buenos Aires [Argentina] and Malta).

The TALIS-PISA link average corresponds to the arithmetic mean of the estimates of participating countries and economies, excluding Viet Nam. Statistically significant coefficients are marked in a darker tone.

Source: Adapted from OECD (2021), Positive, High-achieving Students?: What Schools and Teachers Can Do, TALIS, OECD Publishing, Paris, https://doi.org/10.1787/3b9551db-en, Figure 2.7.

Issue No. 74, July 2021 - The OECD Statistics Newsletter 5

Figure 3.Relationship between teachers’ engagement in extracurricular activities and student perception of classroom disciplinary climate Change in the index of student perception of the classroom disciplinary climate associated with teachers’ engagement in extracurricular activities Change in the index of classroom disciplinary climate

0.25

Positive association between student perception of disciplinary climate and teachers' engagement in extracurricular activities

0.20 0.15 0.10 0.05 0.00 -0.05 -0.10

Malta

Czech Republic

Turkey

Viet Nam

TALIS-PISA link average

CABA (Argentina)

Australia

Georgia

Colombia

Denmark

Notes: Teacher variables are averaged for all teachers within the school.

Positive values on the index of classroom disciplinary climate mean that the student enjoys a better disciplinary climate in language-of-instruction lessons than the average student in OECD countries. The index was scaled with a mean of 0 and a standard deviation of 1 across senated weighted OECD countries.

Results of linear regression based on responses of 15-year-old students and teachers. Controlling for the following classroom characteristics: class size, share of students whose first language is different from the language(s) of instruction, low academic achievers, students with special needs, students with behavioural problems, students from socio-economically disadvantaged homes, academically gifted students, students who are immigrants or with a migrant background and students who are refugees; and for the following student characteristics: gender, immigrant background and index of economic, social and cultural status. TALIS-PISA link data are more likely to provide insights for the Czech Republic and Turkey, where differences in school average performances represent about half of the total variance in student achievements, as opposed to countries, including Australia, Denmark and Malta, where 25% or less of the total variation in student outcomes lie between schools. In addition, it is less likely to have statistically significant results for countries and economies with smaller sample sizes (e.g. Ciudad Autónoma de Buenos Aires [Argentina] and Malta).

Statistically significant coefficients are marked in a darker tone.

Source: Adapted from OECD (2021), Positive, High-achieving Students?: What Schools and Teachers Can Do, TALIS, OECD Publishing, Paris, https://doi.org/10.1787/3b9551db-en, Figure 3.8.

activities, the better students behave in class (Figure 3), the more students report that the teacher is interested and motivated to teach, and that they expect to complete at least a tertiary degree.

However, teachers and school leaders are not the sole stakeholders in student academic and social emotional skills. Students do better when their parents and guardians, and local communities involve themselves in school activities. As expected, classmates seem to matter a great deal for student performance and student self-concept. As the average concentration of students from socio economically disadvantaged homes in the classrooms increases, students tend to perform worse academically and be less likely to aspire to tertiary education studies. In addition, the greater the number of academically gifted classmates enrolled in the classroom, the more a student feels able to succeed in the PISA test and the better they perform on average. While these findings may signal the presence of academic segregation, as high achievers tend to be concentrated in certain schools in most education systems, they can also point to the presence of peer effects. Indeed, a student’s performance can be positively affected by classmates with higher innate ability through an increase in motivation, competition and career aspirations (Sacerdote, 2011[12]). Yet, highperforming students still tend to be less affected than

6 The OECD Statistics Newsletter - Issue No. 74, July 2021

their low-achieving peers by the composition of their classes. This indicates that addressing socio-economic and academic segregation of schools may be beneficial for both increasing student performance at the country level as well as improving equity.

With this report, Positive, High achieving Students? What Schools and Teachers Can Do, the OECD has trialled lasso regressions to shed light on the key factors that can be activated to raise student cognitive and socialemotional outcomes. Similar methods could be applied to any other high-dimensional dataset, in synergy with other approaches, to support better diagnosis for better policies.

References 1. Darling-Hammond, L. (2017), “Teacher education around the world: What can we learn from international practice?”, European Journal of Teacher Education, Vol. 40/3, pp. 291-309, http://dx.doi.org/10.1080/02619768.2017.1315399.

2. OECD (2018), Effective Teacher Policies: Insights from PISA, PISA, OECD Publishing, Paris, https://dx.doi.org/10.1787/9789264301603-en.

3. Chetty, R., J. Friedman and J. Rockoff (2014), “Measuring the impacts of teachers II: Teacher value-added and student outcomes in adulthood”, American Economic Review, Vol. 104/9, pp. 2633-2679, http://dx.doi.org/10.1257/aer.104.9.2633.

4. Jackson, C., J. Rockoff and D. Staiger (2014), “Teacher effects and teacher-related policies”, Annual Review of Economics, Vol. 6, pp. 801 825, http://dx.doi.org/10.1146/annurev-economics-080213-040845.

5. Rivkin, S., E. Hanushek and J. Kain (2005), “Teachers, schools, and academic achievement”, Econometrica: The Journal of the Econometric Society, Vol. 73/2, pp.

417-458, https://doi.org/10.1111/j.1468-0262.2005.00584.x.

9. Ladd, H. and L. Sorensen (2017), “Returns to teacher experience: Student achievement and motivation in middle school”, Education Finance and Policy, Vol. 12/2, pp. 241-279, http://dx.doi.org/10.1162/EDFP_a_00194.

6. Rockoff, J. (2004), “The impact of individual teachers on student achievement: Evidence from panel data”, American Economic Review, Vol. 94/2, pp. 247-252, http://dx.doi.org/10.1257/0002828041302244.

10. OECD (2021), Positive, High-achieving Students?: What Schools and Teachers Can Do, TALIS, OECD Publishing, Paris, https://dx.doi.org/10.1787/3b9551db-en.

7. Jackson, C. (2018), “What do test scores miss? The importance of teacher effects on non-test score outcomes”, Journal of Political Economy, Vol. 126/5, pp. 20722107, http://dx.doi.org/10.1086/699018.

8. Kraft, M. (2019), “Teacher effects on complex cognitive skills and social-emotional competencies”, Journal of Human Resources, Vol. 54/1, pp. 1-36, http://dx.doi.org/10.3368/jhr.54.1.0916.8265R3.

11. Hastie, T., R. Tibshirani and J. Friedman (2017), “The Elements of Statistical Learning: Data Mining, Inference, and Prediction”, in Springer Series in Statistics, Springer, New York, https://web.stanford.edu/~hastie/ElemStatLearn//printings/ESLII_print12.pdf.

12. Sacerdote, B. (2011), “Peer effects in education: How might they work, how big are they and how much do we know thus far?”, in Hanushek, E., S. Machin and L. Woessmann (eds.), Handbook of the Economics of Education, Elsevier, Amsterdam, http://dx.doi.org/10.1016/B978-0-444-53429-3.00004-1.

Tax Autonomy of Subnational Governments Much of the economic and political benefit of decentralised public finance comes from the ability of subnational or sub-central governments (SCGs) to make their own decisions about taxation. A local or regional government that is able to define its own tax bases, tax rates, and other characteristics of a tax has a high degree of tax autonomy or taxing power. To provide accurate cross-national comparisons of the importance of state and local governments in countries’ fiscal systems, it is important to be able to characterise state and local tax systems by their degree of tax autonomy.

Starting in 2002, the OECD began to assess the tax autonomy of state (or regional) and local governments, under the aegis of the Network on Fiscal Relations, on a three yearly basis, with the results made available in the OECD’s Fiscal Decentralisation Database.

A typology indicates the degree of tax autonomy in each country, whereby each tax instrument used by state or local governments in each country is assigned a code indicating the subnational government’s degree of tax autonomy over that tax instrument. The category A codes characterise taxes over which SCGs have the highest level of tax autonomy, setting both tax rates and tax reliefs. Categories B and C cover situations where SCGs have the ability to set either rates or reliefs, but not both. Category D covers tax sharing arrangements. Categories E and F cover situations in which SCGs have no tax autonomy or to which the other codes do not apply. The results are summarised by calculating the share of total government revenue by level of government assigned to each tax autonomy code, based on data from Revenue Statistics.

Tax autonomy in subnational governments, 2018 % of total tax revenues A. The recipient SCG sets the tax rate and any tax reliefs C. The recipient SCG sets tax reliefs E. Other cases

100%

State/regional government

90% 80% 70% 60% 50% 40% 30% 20% 10% 0% 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018

B. The recipient SCG sets the tax rate D. There is a tax-sharing arrangement F. None of the above

100%

Local government

90% 80% 70% 60% 50% 40% 30% 20% 10% 0% 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018

Note: Data shown are the unweighted average for OECD countries, Brazil and South Africa (who are also members of the Fiscal Network). Nine countries have a federal structure: Australia, Austria, Belgium, Brazil, Canada, Germany, Mexico, Spain, Switzerland and the United States. All other countries are treated as unitary.

Source: OECD Fiscal Federalism Database.

Issue No. 74, July 2021 - The OECD Statistics Newsletter 7

Distribution of household income, consumption and saving in line with national accounts Jorrit Zwijnenburg (jorrit.zwijnenburg@oecd.org), Statistics and Data Directorate, OECD

E

conomic inequality continues to be a matter of concern for policy makers and citizens.

The COVID-19 pandemic has re-emphasised the need for more detailed information on how specific household groups are faring economically, particularly during times of crisis. Whereas distributional information is available from micro statistics, it is traditionally missing from macroeconomic statistics.

However, as explained by the Stiglitz-Sen-Fitoussi commission (2009)[1], distributional measures in line with national accounts’ totals may provide important, additional insights to the inequality debate.

down by income quintile, but for several countries also on the basis of household composition and main source of income. The databases also include information on the socio-demographic characteristics of persons and households included in the income quintiles. Furthermore, a working paper has been released (Zwijnenburg et al; 2021[3]), providing further insight into the methodology and highlighting the main results, focusing on thirteen countries, i.e. Australia, Canada, the Czech Republic, France, Ireland, Israel, Italy, Mexico, the Netherlands, New Zealand, Slovenia, Sweden, the United Kingdom and the United States.

In 2011, the OECD and Eurostat started developing methodology to compile disparities in line with national accounts totals (DNA) for income, consumption and saving (see OECD, 2020 [2]). These estimates complement existing inequality measures by providing more comprehensive measures of inequality, extending the analysis from income to consumption and saving, and by yielding results that are fully consistent with macroeconomic aggregates, ensuring a high degree of international comparability.

Income inequality

Over the past few years, member states have begun to compile experimental results according to the DNA methodology and, as of the end of 2020, these results have been included in the public databases of the OECD and Eurostat for a first time.1 Results are available broken

The DNA results provide information on the level of income inequality within and across countries. Figure 1 displays the relative adjusted disposable income of five income quintiles for six member states. Mexico reports the highest ratio to the average of the household sector as a whole, followed by the United States, with households in the highest income group earning 2.99 and 2.44 times the average, respectively. Sweden reports the lowest ratios for this group, at 1.54 times the average. For the first quintile, the United States and Mexico report the lowest ratios (with only 33% and 35% of the average), whereas the United Kingdom records the highest, at 61% of the average. The trend across

Figure 1. Relative position of each household group compared to the average, by equivalised disposable income quintile

Figure 2. Relative position of the highest to the lowest income households, by equivalised disposable income quintile

Canada 2015 United Kingdom 2015 Sweden 2015

3.5

9.0

France 2015 Mexico 2016 United States 2015

8.0 7.0 6.0

3

5.0

2.5

4.0 3.0

2

2.0

1.5

1.0

1

0.0

0.5 0

Q1

Q2

Q3

Q4

Q5

Source: Zwijnenburg, J., et al. (2021), "Distribution of household income, consumption and saving in line with national accounts: Methodology and results from the 2020 collection round", OECD Statistics Working Papers, No. 2021/01, OECD Publishing, Paris, https://doi.org/10.1787/615c9eec-en.

8 The OECD Statistics Newsletter - Issue No. 74, July 2021

quintiles is more or less the same for all countries, with a fairly smooth trend upwards from the first quintile to the fourth quintile, and a steeper increase from the fourth to the fifth quintile. Furthermore, for all countries, the ratio for the third quintile is below 1. As this can be used as an approximation for the median, this implies that the median equivalised adjusted disposable income is below average in all countries. For Mexico and the United States even the equivalised adjusted disposable income of the fourth quintile is below average. To provide additional insight into the relative income inequality across countries, Figure 2 presents the relative difference in income between the highest and the lowest income group for all twelve countries that provided this information. Mexico is the country recording the highest ratio (8.56), followed by the United States (7.41). The other countries are relatively closer together, reporting much lower ratios.

Ireland records the lowest (at 2.70), followed by Sweden (2.78), the United Kingdom (2.82), Slovenia (2.84), the Netherlands (2.90) and the Czech Republic (2.91), all recording ratios below 3.

The working paper and databases also include information on the underlying income items. This shows that rising self-employment income and property income (such as dividends and interest income) have been the main drivers of income inequality. Conversely, compensation of employees has tended to be more stable across countries. Furthermore, taxes, social benefits and social contributions have a strong mitigating effect on income inequality in all countries, though to differing extents. For example, for Australia, the ratio of the highest quintile to the lowest drops from 11.6 for primary income to 3.1 for adjusted disposable income, whereas it only drops from 11.7 to 7.4 in the United States. This detailed information provides very relevant evidence for policymakers.

Figure 3. Saving as a percentage of disposable income by equivalised disposable income quintile Australia 2015 France 2015 New Zealand 2015

%

Consumption inequality For consumption, the disparities across quintiles are generally smaller and the results for the various income quintiles closer to the average than for adjusted disposable income. As for income, the United States and Mexico record the highest ratios for the fifth quintile, at 2.29 and 2.18 times the average, respectively. Furthermore, the United States records, by far, the lowest ratio for the first quintile, with consumption amounting to only 32% of the average, followed by Mexico with households in the first quintile recording consumption at 50% of the average. The Netherlands records the highest ratio for the first quintile, at 85% of the average. Turning to the ratio of the highest quintile to the lowest, Sweden records the lowest disparities between the highest and lowest income quintile (1.42), followed by the Netherlands (1.47), the Czech Republic (1.48) and Slovenia (1.53), whereas the United States (7.15) and Mexico (4.37) record the highest. For all countries, these ratios are lower than for income. Looking at underlying consumption items, the items related to basic needs (i.e. food and beverages, and housing) show relatively flat distributions across income quintiles for all countries, whereas items such as recreation, restaurants and hotels, and furnishings show relatively larger disparities. The same can be observed for education and health care, although largely depending on whether or not these services are (partially) provided by government.

Savings results While distributional patterns are more or less similar for income and consumption, saving results show larger differences across countries, particularly for the lowest income quintile. Figure 3 shows saving as percentage of Figure 4. Composition of the private household sector saving ratio % 30

Czech Republic 2017 Mexico 2016 Slovenia 2015

25

60

20

40

15

Quintile 5 Quintile 4

10

20

5

0

0

-20

-5

-40

-10

-60

-15

-80

Quintile 3 Quintile 2 Quintile 1

-100 -120

Q1

Q2

Q3

Q4

Q5

Source: Zwijnenburg, J., et al. (2021), "Distribution of household income, consumption and saving in line with national accounts: Methodology and results from the 2020 collection round", OECD Statistics Working Papers, No. 2021/01, OECD Publishing, Paris, https://doi.org/10.1787/615c9eec-en.

Issue No. 74, July 2021 - The OECD Statistics Newsletter 9

disposable income per income quintile, for six countries.

New Zealand displays a very large negative saving ratio for the first quintile with a big jump to the second quintile, although still negative. Similar results can be observed for Canada, the Netherlands and Sweden. The other countries show smoother increases across quintiles, starting from less negative saving rates. France stands out with the most stable saving ratios across quintiles, with particularly small negative saving ratios for the first and second quintile, as compared to the other countries.

Delving further into the relative size of savings across quintiles, Figure 4 examines the contribution of the different quintiles to the saving ratio for the household sector as a whole. New Zealand records the lowest overall saving ratio (2.1%), with the lowest three quintiles contributing negatively. Canada and the Czech Republic also report low saving ratios for the household sector as a whole (4.4% and 4.7% respectively), but while in Canada this is due largely to particularly negative savings for the first quintile, in the Czech Republic this is due to the first four income quintiles recording negative savings. The latter is also the case for Mexico, but they still report the highest average saving ratio for the household sector as a whole (23.5%), due to very

high savings ratio for the fifth quintile. Sweden and the Netherlands also record large positive saving ratios for the household sector as a whole, but in contrast to Mexico, this is not particularly related to substantial saving of the fifth quintile, but due to positive saving by households in the third and fourth quintile.

Socio-demographic information The databases also include information on the sociodemographic characteristics of persons and households included in the various income quintiles, focusing on breakdowns by age group, labour market status, level of education and housing status. This provides additional insights into the background of the persons and households along the income distribution.

For example, several countries record a higher concentration of younger people in the lower income quintiles, whereas the picture is more mixed for other age groups, with the United Kingdom recording a large concentration of people over 65 in the lower income quintiles, while this group tends to be more concentrated in the fourth and fifth quintile in the United States (see Figure 5). Additionally, the concentration of certain labour market status groups shows important differences across

Figure 5. Distribution of age groups across quintiles

Netherlands 2015 100%

Q5

80%

Q4

60%

Q3

40%

Q2

20% 0%

New Zealand 2015

Q1

100%

Q2

20%

Q1 0-14 15-24 25-34 35-44 45-64 65+

United States 2015 Q5

80%

Q3

40%

United Kingdom 2015 100%

Q4

60%

0%

0-14 15-24 25-34 35-44 45-64 65+

Q5

80%

Q4

100%

Q5

80%

Q4

60%

Q3

60%

Q3

40%

Q2

40%

Q2

20%

Q1

20%

Q1

0%

0-14 15-24 25-34 35-44 45-64 65+

0%

0-14 15-24 25-34 35-44 45-64 65+

Source: Zwijnenburg, J., et al. (2021), "Distribution of household income, consumption and saving in line with national accounts: Methodology and results from the 2020 collection round", OECD Statistics Working Papers, No. 2021/01, OECD Publishing, Paris, https://doi.org/10.1787/615c9eec-en.

10 The OECD Statistics Newsletter - Issue No. 74, July 2021

countries. For example, retired people tend to be more concentrated in the lower income quintiles in Israel, but the opposite is true for Mexico. Furthermore, whereas all countries show a relatively large representation of employers in the fifth quintile, Mexico and Slovenia also record substantial shares in the first quintile. More detailed information on the various socio-demographic breakdowns can be found in Zwijnenburg et al. (2021).[3] Next steps The DNA results have been made available in the public databases of the OECD and Eurostat as experimental statistics. This provides users with the opportunity to explore these results in more detail and to conduct their own analyses. In the meantime, the DNA work continues.

Looking ahead, the main objective is to further improve the timeliness and granularity of the results, to broaden the country coverage, and to extend the scope to include

the wealth dimension. This will further increase the relevance of the results for policy analysis.

Notes 1. See http://www.oecd.org/sdd/na/household-distributional-results-in-line-withnational-accounts-experimental-statistics.htm and https://ec.europa.eu/eurostat/web/ experimental-statistics/ic-social-surveys-and-national-accounts respectively.

References 1. Stiglitz, J.E., A. Sen and J.P. Fitoussi (2009), “Report by the commission on the measurement of economic performance and social progress”, http://library.bsl.org.au/jspui/bitstream/1/1267/1/Measurement_of_economic_performance_and_social_progress.pdf.

2. OECD (2020), “Distributional information on household income, consumption and saving in line with National Accounts – Guidelines”, https://www.oecd.org/sdd/na/OECD-EG-DNA-Guidelines.pdf.

3. Zwijnenburg, J., et al. (2021), "Distribution of household income, consumption and saving in line with national accounts: Methodology and results from the 2020 collection round", OECD Statistics Working Papers, No. 2021/01, OECD Publishing, Paris, https://doi.org/10.1787/615c9eec-en.

APO-OECD Report: Towards Improved and Comparable Productivity Statistics Addressing measurement challenges is crucial if economies are to develop sound policies to navigate out of the current crisis. Certain challenges in productivity measurement – for example, the measurement of the non-observed economy, non-observed employment, and the self-employed – are more severe in many economies that are members of the Asian Productivity Organisation (APO).

Towards Improved and Comparable Productivity Statistics A SET OF RECOMMENDATIONS FOR STATISTICAL POLICY

The first report of the APO and OECD explores current practices and challenges in productivity measurement and provides recommendations to National Productivity Organisations (NPOs), National Statistics Offices (NSOs) and other agencies involved in the compilation and analysis of productivity statistics in APO member economies to improve measurement and cross-country comparability.

Issue No. 74, July 2021 - The OECD Statistics Newsletter 11

Enterprises in Global Value Chains The recent OECD-Statistics Finland collaboration brings a fully integrated approach to capture firm heterogeneity in global value chains (GVCs) and to measure the impact of GVCs on Finnish businesses and the Finnish economy. It has established a statistical foundation combining business statistics, social statistics, and national accounts data into a consistent framework, allowing policy makers to address some issues regarding the impact of globalisation on competitiveness, employment, and income and gender inequality. Developing further the first joint publication, Globalisation in Finland: Granular insights into the impact on businesses and employment (2020), two additional reports, financed by the Finnish Ministry of Foreign Affairs, were released in May 2021. The first report assesses how granularity has improved the quality of globalisation indicators for Finland, while the second outlines how the COVID-19 pandemic has variously affected industries, businesses and their employees.

MEASURING TRADE IN VALUE-ADDED FOR FINLAND: USING GRANULAR VALUE-ADDED TRADE ESTIMATES For Finland, more disaggregated input-output tables than those in the standard TiVA database reveal that the share of foreign content in total exports – an indicator of GVC integration – is 10 percentage points higher than estimated earlier. The level of integration is even higher for some industries, and for some types of firms.

Key findings • Across sectors, Finnish exports are much more dependent on foreign inputs than is shown in OECD’s TiVA database.

However, domestic output is slightly less dependent on foreign demand.

• Despite Finland’s economy shifting towards knowledge-based services in recent years, traditional manufacturing industries remain significant contributors to gross and domestic value-added exports, and they still employ a significant number of workers.

• In addition to firm size, enterprise group dependency plays a significant role in determining foreign market participation: for a given unit of output, dependent SMEs are likely to use disproportionately more foreign inputs and skilled employees than independent SMEs. • Greater granularity in TiVA estimates makes for improved and more targeted policymaking.

FINLAND: ROAD TO RECOVERY AFTER COVID-19 The economic situation in Finland during the COVID-19 crisis turned out to be less negative than OECD’s early prediction. The magnifying effects of forward and backward linkages – through global value chains – also started to tail off. By the end of 2020, the manufacturing sector had nearly restored its confidence in production with business sales picking up. The services sector, however, still faces lingering effects of the pandemic due to mobility restrictions that are still in place. But bucking the trend, ICT industries in Finland, in both the manufacturing and services sectors, have gained an advantage during this crisis.

The Finnish government’s effective crisis management has mitigated the shocks. For instance, adequate financial support was available for furloughed workers and businesses in financial difficulties. Moreover, temporary changes in legislation have helped businesses to stay afloat during the crisis. In part because international merchandise trade regained momentum and the business confidence level began to bounce back, OECD projects a full economic recovery in the coming two years.

Key findings • Current business data show that the Finnish economy has demonstrated a “K-shaped” recovery path: while business turnovers have returned to pre-crisis levels in most manufacturing industries, only some services industries have seen sales picking-up.

• Young, female, low-skilled and low-income workers are overly represented among the furloughed workers.

• Some firms in heavily affected industries, especially SMEs with low turnover volumes, have been hit hard by the pandemic. However, SMEs seem less prone to shocks in trade than large businesses, though they are affected indirectly.

• The Finnish government provided adequate financial support in conjunction with legal instruments, monetary and fiscal tools, protecting jobs and incomes

12 The OECD Statistics Newsletter - Issue No. 74, July 2021

Improving the quality of foreign aid data through machine learning Shashwat Koirala (shashwat.koirala @oecd.org), Development Co-operation Directorate, Pedro Asti (pedro.asti@oecd.org) and Jan-Anno Schuur (Jan-Anno.schuur@oecd.org), Digital, Knowledge and Information Service, Executive Directorate, OECD

T

he OECD Creditor Reporting System (CRS) is an activity-level database that enables analysis on where foreign aid goes, what purposes it serves and what policies it aims to implement. It contains financial and qualitative data, as well as project descriptions, for each individual aid project or programme. During the annual data collection process, the OECD ensures that the reported data meet the measurement standards for development finance. This includes using the descriptive information for each project to verify the accuracy of key reporting variables (e.g. sector codes), which is time-intensive, as it entails manually reading individual project descriptions.

The OECD’s Development Cooperation Directorate (DCD) and the Digital, Knowledge and Information Service, (DKI) are collaborating on a semantic analysis tool to automate the verification of reported CRS sector codes. This tool, which will be launched in 2021, will not only speed up the data validation process, but also enhance the quality of CRS data. In addition, it will become part a free web-based service, which will allow donors to submit a text string (whether manually or through an Application Programming Interface) to validate an existing sector code or obtain suggestions for more appropriate codes, thereby improving the alignment between purpose codes and project descriptions even before the data are reported to the OECD.

CRS and the use of purpose codes to track sectoral focus of aid The CRS captures, on average, 250,000 aid activities per year, which are reported by 30 Development Assistance Committee (DAC) members, about 40 multilateral institutions, 25 non-DAC providers and 41 private foundations. For each aid activity, the database includes information along around 60 dimensions, broadly grouped into six categories (see Figure 1). In addition to data on the financial aspects of aid, the CRS consists of information on the targeted sector, channel of delivery, co-operation modality, targeted policy and environmental objectives, as well as project titles, short and long descriptions that summarise the main objectives of the project.

A crucial element of the CRS database are purpose codes, which convey information on a given project’s sector of focus. These five-digit codes distinguish between not only broad sectors (e.g. education versus health), but also delineate different activities within the same sector (e.g. secondary education versus higher education). This enables a granular understanding of an aid provider’s activities and facilitates detailed statistical analysis as to which sectors and sub-sectors the aid is targeting. This information is used, for example, to monitor aid targets like SDG 4.b.1 on the volume of official development assistance flows for scholarships by sector.

Figure 1. Components of the CRS database

CREDITOR REPORTING SYSTEM DATABASE

IDENTIFICATION DATA (e.g., commitment date, donor country or organisation)

BASIC DATA (e.g., recipient country, short description, sector/purpose code)

SUPPLEMENTARY DATA (e.g., long description, policy objectives)

VOLUME DATA (e.g., commitment amount, disbursement amount)

LOAN DATA (e.g., interest rates, repayment period)

PRIVATE SECTOR INSTRUMENT DATA

If applicable

Issue No. 74, July 2021 - The OECD Statistics Newsletter 13

There are currently 234 CRS purpose codes within several broad sector areas (see Figure 2 , which shows the different purpose codes within the health sector).

The number of purpose codes fluctuates as policy and/ or statistical needs shift and purpose codes are added or removed for future reporting. For example, to better track financing for COVID-19 related efforts, purpose code 12264 (“COVID-19 control”) was introduced for reporting on 2020 development finance flows.

Value-added of semantic technology To augment CRS data quality in an automated manner, the semantic tool leverages different semantic algorithms that allow for the extraction of knowledge from unstructured data (in this case, the CRS’s descriptive fields). Technically, such a tool is called a cartridge: it can very rapidly read texts, identify specific linguistic patterns and rules, and effectively function as a reading assistant. Based on the underlying algorithms, the cartridge outputs plausible purpose codes for each CRS project.

This approach has several benefits: • It saves the analyst time. Instead of searching and reading all available material, the analyst can concentrate on the interpretation of the relevant material. The machine does not replace the human; it merely assists.

• It is completely transparent. The semantic reasoning behind the results can be explained clearly and precisely. The different steps, from the text analysis to the identification by the machine of a specific purpose code, can be fully demonstrated. This avoids the typical ‘black box’ problem of artificial intelligence tools where it is virtually impossible to ascertain how the machine arrives at its conclusion in a particular case. Transparency towards member countries is fundamental to the OECD’s work.

• It is rigorously consistent (though this can be good or bad). Unlike human interpretation, which may differ depending on the time of day (or the weather), a sentence is always interpreted in the same way by a cartridge. Consequently, the results are perfectly homogenous, whether they are right or wrong.

Introducing Cogito The technical solution used for the project is a semantic analysis software named Cogito, developed by the Italian company Expert.ai. This solution is used at the OECD for corporate semantic enrichment services.

It is integrated with the central OECD taxonomies to identify and annotate concepts, such as geographical areas, organisations and topics in texts, as well as their synonyms and alternate expressions. These annotations are currently used for search and discovery purposes in several OECD knowledge bases.

Figure 2. Purpose codes in the health sector Research for prevention and Health policy and administrative management control of NCDs

Other prevention and treatment of NCDs

Medical education/training

Promotion of mental health and well-being

Medical research

Control of harmful use of alcohol and drugs

Medical services

Tobacco use control

Basic health care

HEALTH

Basic health infrastructure

NCDs control, general

Health personnel development

Basic nutrition

COVID-19 control Tuberculosis control

14 The OECD Statistics Newsletter - Issue No. 74, July 2021

Infectious disease control Malaria control

Health education

The particularity of the Cogito tool lies in its use of an all-encompassing ontology of the language as the basis for text analysis. This means that the tool provides a sophisticated ‘semantic engine’ that is capable of a very good understanding of the English (or French) language (including its grammar!). Language is often ambiguous; one of the main challenges in natural language processing (NLP) is making this understandable to machines, which usually operate under a strict ruleset. The semantic architecture of Cogito delivers a solid foundation from which to start. At the OECD, this capability is used to ensure a very high level of accuracy in the tagging with the concepts from our central taxonomies – typically, the quality of these tags is around 99 % precision and recall.

The use of Cogito to improve purpose code assignment in the CRS consists in exploiting three interconnected layers of semantic reasoning to create a very flexible system that automates the identification of topics in a sample of text.

• the first layer is the English language understanding in Cogito, which has been aligned with • the second layer of the corporate OECD taxonomies that encompass how the OECD sees the world expressed in English language, which is aligned with • the third CRS codes specific layer that, in effect, enables the machine to understand OECD English with a CRS twist.

Process and methodology The first step in the exercise involved generating a corpus of text used to develop the semantic tool. This corpus of text consisted of a sample of project descriptions for each purpose code from previously reported CRS data.

Given the wide variance in the quality of descriptive information provided by donors, the corpus was restricted to data from donors identified by the Secretariat as providing reliable and relevant project descriptions. To best reflect current reporting tendencies – including the purpose codes used by donors – the corpus was based on data from the last three available reporting years (i.e., 2016, 2017 and 2018). Finally, while donors report project descriptions in English, French and sometimes in their native language, the corpus and subsequently, the semantic tool, was initially limited to English language descriptions.

Having narrowed the scope, a random sample of 50 project descriptions for each purpose code was drawn.

This formed the text corpus for the development of the semantic tool, which was used as the basis for the following steps: • annotate the corpus to identify topics from the central OECD taxonomies; • identify relevant rules for each CRS purpose code, using as much as possible OECD Topics-based alignments, completed where necessary with other

Figure 3. The three semantic layers CRS English language understanding Education policies and administrative management CRS Sector code: 11110

CRS Sector Codes Alignment

Education facilities and training CRS Sector code: 11120

OECD English language understanding

Teachers URI: T1347

OECD Taxonomies Alignment

Education URI: T4474

Schools URI: T1292

English language understanding

Cogito Ontology

Instructor ID: 47737

Educator ID: 44865

Education ID: 27311

School ID: 38277

Issue No. 74, July 2021 - The OECD Statistics Newsletter 15

linguistic elements and joined together with the help of various logical operators (e.g. AND, AND NOT, OR); • implement these rules in a specific semantic analysis cartridge for CRS code identification; • test the tool on ‘test corpora’ of texts to assess the quality of the code identification; and • improve the rules based on an iterative validation process, combining subject-matter and technical expertise Conclusion As the only source of reliable, comparable and complete activity-level data on development assistance, the Creditor Reporting System is indispensable for

practitioners and researchers working on development.

The use of semantic technology enhances the CRS’s accuracy and consistency – and consequently, the aid statistics calculated using its data – while delivering efficiency gains for the OECD. Following the initial development phase, the tool can be updated to reflect changes to the CRS purpose codes.

It is planned to extend the tool to other fields of the database, such as policy and Rio markers, which help flag projects that target crosscutting policy objectives (e.g., gender equality, reproductive, maternal, new-born and child health, trade development) and environmental priorities (e.g., biodiversity, climate change mitigation, climate change adaptation). This means that the semantic tool is likely to engender sustained improvements in the quality of foreign aid data.

Recent publications OECD SME and Entrepreneurship Outlook 2021 ‌ MEs, self-employed and entrepreneurs have been hard hit during the COVID-19 crisis. Despite S the magnitude of the shock, available data point to vigorous business dynamism, sustained start-up creation, no major increase in bankruptcies. The crisis has also served as a catalyst for (some forms of) innovation and entrepreneurship in most countries. The 2021 OECD SME and Entrepreneurship Outlook provides new evidence on the critical role governments and their support packages have played in cushioning the blow. It also stresses that not all people, places and firms have been able to get support as needed, but those who did have performed better.

OECD (2021), OECD SME and Entrepreneurship Outlook 2021, OECD Publishing, Paris. https://www.oecd.org/publications/oecd-sme-and-entrepreneurship-outlook-2021-97a5bbfe-en.htm

Agricultural Policy Monitoring and Evaluation 2021 Agricultural support has continued to grow worldwide in recent years, but is often failing to meet its stated aims of improving food security, livelihoods and environmental sustainability, according to a new report from the OECD. Agricultural Policy Monitoring and Evaluation shows that the 54 countries monitored - including all OECD and EU economies, plus 11 key emerging economies – provided on average USD 720 billion of support to agriculture annually over the 2018-20 period.

OECD (2021), Agricultural Policy Monitoring and Evaluation 2021, OECD Publishing, Paris. https://doi.org/10.1787/2d810e01-en

16 The OECD Statistics Newsletter - Issue No. 74, July 2021

Recent publications OECD Skills Outlook 2021 Countries must step up their efforts to enable people to continue learning throughout their lives to navigate a rapidly changing world of work shaped by globalisation and the consequences of the COVID-19 pandemic, according to a new OECD report.

OECD Skills Outlook 2021: Learning for Life says that public policies should play a key role in facilitating effective and inclusive lifelong learning, but much remains to be done.

It will be crucial to invest part of the resources devoted to the recovery to lifelong learning programmes, involving all key stakeholders and with a focus on vulnerable groups, particularly young people, the NEET (neither in employment, education or training) and those whose jobs are most at risk of transformation, says the report.

OECD (2021), OECD Skills Outlook 2021, OECD Publishing, Paris https://www.oecd.org/skills/oecd-skills-outlook-e11c1c2d-en.htm

OECD Economic Outlook, Volume 2021 Issue 1 Prospects for the world economy have brightened but the recovery is likely to remain uneven and, crucially, dependent on the effectiveness of public health measures and policy support, according to the OECD’s latest Economic Outlook.

In many advanced economies more and more people are being vaccinated, government stimulus is helping to boost demand and businesses are adapting better to the restrictions to stop the spread of the virus. But elsewhere, including in many emerging-market economies where access to vaccines as well as the scope for government support are limited, the economic recovery will be modest.

OECD (2021), OECD Economic Outlook, Volume 2021 Issue 1, OECD Publishing, Paris https://oecd.org/economic-outlook

ITF Transport Outlook 2021 The ITF Transport Outlook 2021 provides scenarios for the development of transport demand up to 2050. It also models transport decarbonisation scenarios and their impacts on climate change.

Based on this, the ITF Transport Outlook 2021 identifies decisions that policy makers will need to take to ensure a transition to sustainable mobility that is effective as well as equitable.

This edition includes a special focus on the impacts of the Covid-19 pandemic on transport systems, and models potential long-term changes with challenges and opportunities for decarbonisation.

ITF (2021), ITF Transport Outlook 2021, OECD Publishing, Paris. https://www.itf-oecd.org/itf-transport-outlook-2021

Issue No. 74, July 2021 - The OECD Statistics Newsletter 17

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

OECD Date

Meeting

5 July

Launch of the OECD-FAO Agricultural Outlook 2021-30, Trade and Agriculture Directorate, OECD 7 July 2021 Launch of the OECD Employment Outlook 2021, Directorate for Employment, Labour and Social Affairs, OECD 8-9 July 2021 Annual Conference of the Global Forum on Productivity co-organised by the Italian G20 Presidency, Venice, Italy, https://www.oecd.org/global-forum-productivity/events/2021annualconferenceoftheglobalforumonproductivityjointwithitalianpresidencyofg20.htm 23-29 September 2021 2nd Workshop on Time Series Methods for Official Statistics, Statistics and Data Directorate, OECD 27 September 2021 Transport Statistics, International Transport Forum 4-8 October 2021 Working Party on International Trade in Goods and Services Statistics (WPTGS), Statistics and Data Directorate, OECD 11-13 October 2021 Expert Group meeting on Extended Supply and Use Tables, Statistics and Data Directorate, OECD 20-22 October 2021 Working Party on Indicators of Educational Systems (INES), Directorate for Education and Skills, OECD 25-29 October 2021 Working Party on National Accounts (WPNA), Statistics and Data Directorate, OECD 26-28 October 2021 10 November 2021 18 November 2021 19-22 November 2021 23-24 November 2021 24 November 2021 8 December 2021 13-15 December 2021 21-23 March 2022 22-24 March 2022 23-25 March 2022 24-25 March 2022

Working Group on International Investment Statistics (WGIIS), Directorate for Financial and Enterprise Affairs, OECD 7th Meeting of the Working Party for the OECD Patient Reported Indicator Surveys (PaRIS), Directorate for Employment, Labour and Social Affairs, OECD Working Party No. 2 on Tax Policy Analysis and Tax Statistics, Centre for Tax Policy and Administration, OECD Working Group on International Investment Statistics (WGIIS), Directorate for Financial and Enterprise Affairs, OECD Informal meeting of the DAC Working Party on Development Finance Statistics (WP-STAT), Development Co-operation Directorate, OECD Working Party on Territorial Indicators, Centre for Entrepreneurship, SMEs, Regions and Cities, OECD - Paris, France Task Force on Pension Statistics, Task Force on Pension Statistics Working Party of National Experts on Science and Technology Indicators (NESTI), Directorate for Science, Technology and Innovation, OECD Working Party on International Trade in Goods and Services Statistics (WPTGS), Statistics and Data Directorate, OECD Working Group on International Investment Statistics (WGIIS), Working Group on International Investment Statistics (WGIIS) Working Party of National Experts on Science and Technology Indicators (NESTI), Directorate for Science, Technology and Innovation, OECD 2nd Workshop on Time Series Methods for Official Statistics, Statistics and Data Directorate, OECD

Other meetings 9-10 July 2021 22 July 2021 12 October 2021 15-17 October 2021 30-31 October 2021

G20 - 3rd Finance Ministers and Central Bank Governors Meeting G20 - Environment Ministers’ Meeting G20 - Trade Ministers’ Meeting Annual Meeting of the World Bank Group and the International Monetary Fund, https://www.worldbank.org/en/meetings/splash/annual G20 Heads of State and Government Summit, https://www.g20.org/en/vertice-di-roma.html

18 The OECD Statistics Newsletter - Issue No. 74, July 2021

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