1. Making Statistics Talks 1&2 - Effective reporting of statistical results PPTs1+2
CONTRACTOR IS ACTING UNDER A FRAMEWORK CONTRACT CONCLUDED WITH THE COMMISSION

2. Course overview
Principles for writing a statistical report; identify & convey the key message
Using graphics to convey a statistical message
Introduction to Eurostat’s ‘Flexible dashboard’ tool and other similar tools
Course focus is on Statistics Explained; reference to longer statistics reports

3. Principles for writing a statistical report
Write for the audience – majority non-statisticians
Comprehensive coverage of data developments
Identify the main story
The borderline between explanation and analysis

4. Statistics explained An encyclopaedia on European Union statistics;
A portal to further information for occasional and for regular users;
A statistical glossary.
Wiki structure designed for collaborative writing
Statistical articles are proposed by an expert in a given field, but can then be reviewed easily by his/her colleague or supervisor. They can be revised by different persons under different angles, for example by a statistical expert on content, by a native English speaker on language, by a technical person on layout and presentation, and by a reviser on coherence or political correctness.
Source: Statistics Explained – a user-friendly and low-cost dissemination system, Ulrich Wieland, Eurostat

5. Statistics Explained - objectives
Initial purpose is to provide information to users about Eurostat’s data and metadata
data overview, metadata and non-technical explanations
complements, gateway to technical information
Increasing use is as Eurostat’s primary medium to disseminate statistics
current information that requires more frequent update
this objective can more easily be met with less frequent statistics series
Statistics Explained is an online publishing system about EU statistics which uses MediaWiki technology and resembles Wikipedia. It is a wiki-based system that presents statistical articles which together form an encyclopaedia of European statistics, completed by a glossary of the statistical concepts and terms used. In addition, numerous links are provided to the latest data and metadata and to further information, making Statistics Explained a portal for regular and occasional users alike.
Less frequent statistics series are here meant annual or less.

6. Statistics explained audience
Expert & non-expert users - occasional and regular visitors
As with most other statistics publications
Experts
Require accuracy
Non-experts
May have specialist knowledge in either subject matter being covered or statistics
Technical terms need explaining to non-experts
Referring to the glossaries
Journalists not targeted - will use press releases as primary information source

7. Statistics explained structure
Summary paragraphs in plain English
Main statistical findings
Data sources and availability
Context
Further Eurostat information – direct links
External links
Main statistical findings highlight the most important statistical results, both through text and through graphs and tables.
Data sources and availability briefly describes how the data were obtained (for example, by which survey) as well as potential limitations and problems.
Context discusses reasons behind the data collection and the uses that may be made of the data; it may refer to the legal basis, the policy context, the importance for politics, business or the society as a whole.
Further Eurostat information provides direct links to more detailed or more recent information on the Eurostat website including freshest data, publications, or detailed methodological information.
External links to related information from other institutions and organisations.
Hint: Suggest trainees to find a statistics explained page that has this structure and follow the comments while looking at it.

8. Statistics Explained – do examples shown conform to structure?
Inflation in the Euro area
Partially conforms to structure – most data sources and context on different pages
Maritime ports freight and passenger statistics
Conforms to structure
Tourism industries - economic analysis

9. Data sources and availability
Describes the characteristics of the statistics
Answers the question, ‘What is being measured?’
‘Static’ information (as in a database static table): it should not need to be edited very often

10. Data sources and availability 1
Sources: identify surveys / administrative data collection; frequency
How measured - classification (nomenclature) and common aggregates
Structure can be illustrated by graphic of weights structure
Refer to EU and UN manuals / regulations
Coverage of the main data series
Identify recent changes

11. Data sources and availability 2
Convey inherent variability and/or uncertainty of data, where appropriate
Especially in relation to disaggregation
Should refer to Eurostat’s concepts and definitions database - Ramon
If major methodological explanations are needed for a wide audience, they can be written on dedicated pages (with links)
Inherent variability – when the change between two consecutive data releases typically show large variations, especially at disaggregated levels. Example: external trade.

12. Statistics Explained: Do examples shown of Data sources and availability cover the subject?
Material deprivation statistics - early results
Information partly present in section, disjointed.
Maritime ports freight and passenger statistics
Partly present, some key information missing
Tourism industries - economic analysis
Good but still not a complete overview

13. Exercise 1: Data sources and availability
For an area of statistics that you know:
What are the data sources (i.e. usually Member States): surveys / administrative data; frequency
What are the relevant statistical classifications?
What are the relevant international manuals and EU regulations?
What are the main data series? Data problems?
What links (Eurostat / external) do you include?
10 minutes discussion
If you don‘t know the answers, write down the information sources that you would use to find them.

14. Context Reasons behind the data collection
Uses of the data; legal basis
Policy context: politics, business, society
Historical evolution of the data
Long-term structure, especially Member States
‘Static’ information – changes infrequently
Where potential exists for inappropriate demands for data: example – geographical disaggregation of national accounts. Data sources often do not exist; compilation costs are high and interpretation can be difficult. Regional production and employment data provide alternative sources for demands for regional activity measures.

15. Context 2 Relationship to other data series
For example where survey and administrative data both exist
Cross-refer to other data series where potential exists for inappropriate demands for data
Can refer to non-state data sources
E.g. Industry business surveys
Arguably, this is where the description of historical trends belongs

16. Context 3: structure Illustrate classification structure
Weights of the main components of the euro area HICP – 2014
Arguments for / against showing full COICOP12?
In favour – provides full information about consumption structure
Against: - pie-charts with >5 items are difficult to grasp visually

17. Context 4: structure Main European cargo ports in 2012 by gross weight of goods handled

18. Context examples 1: Maritime ports freight and passenger statistics
Legal framework only; structure and historical trends are in ‘Main statistical findings’
2: Tourism industries - economic analysis
Presents area covered and data sources. Structure is covered in ‘Main statistical findings’
3: Coal consumption statistics
No context. Further Eurostat Information links

19. Exercise 2: Context For an area of statistics that you know:
Why is the data collected?
What are the main uses of the data?
Where applicable, what is the legal basis for data collection (Directive)?
What is the policy context: politics, business, society?
10 minutes discussion

20. Exercise 3: Context again
What related data series exist?
E.g. survey and administrative data both exist
For the data series in question, are there known inappropriate uses? What other data series might be relevant to users?
Are there any non-state data sources?
E.g. Industry business surveys
What is the structure of the data, especially concerning Member States?
Describe the long term trends in the data?
10 minutes discussion

21. Summary section
Approximately 40 first words of latest posts appear on the Statistics Explained Main Page – should refer to Main statistical findings
Press release can be a good source
One or two paragraphs to summarise the static pages - data sources and availability / context

22. Further Eurostat Information / External Links
Links provided within Eurostat website and to external websites
Links need to be organised and explained
Static panel
Example:
Unemployment statistics

23. Main statistical findings
Coverage of current data developments – dynamic: can change monthly
Covers data developments comprehensively
Explains the data changes without analysing external causes
Biggest effect and Biggest surprise
Dates need to be identifiable:
Release date for statistics under discussion
Reference date for these statistics
Update date for ‘Findings’ – since the page could potentially be altered after first release

24. Main statistical findings - examples
1. Coal consumption statistics
The little current data quoted is presented at the end of each section
2. Inflation in the euro area
Summary findings plus information that should go under other headings
3. Unemployment statistics
Comprehensive coverage but longer term trends could be in Context section and older analysis could be edited 3:

25. Main statistical findings - Analyses
Identify and communicate the key message:
Change in main aggregate data
Contributions to changes in the aggregate data
Avoid contentious analysis
Base period analysis
Disaggregation
Trend changes
Volatility and accuracy

26. Main statistical findings -
Aggregate changes (EU & Eurozone):
Direction, size of aggregate change over year (or other reasonably long period),
Changes from previous period (month to month)
Place data in context of at least a year’s observations
Change in trend (to be considered later)
Member State comparisons
range of outcomes and trend changes
Period comparisons as above
Why might analysis from historical base date be misleading?

27. Example of data developments: HICP Harmonised index of consumer prices
geo\time
2013M03
2013M04
2013M05
2013M06
2013M07
2013M08
2013M09
2013M10
2013M11
2013M12
2014M01
M02e
M/M-1
EU (28 countries)
0.9
0.1
-0.4
0.4
-0.1
0.3
-0.9
Euro area (18 countries)
1.2
-0.5
0.5
-1.1
M/M-12
1.9
1.4
1.6
1.7
1.5
1.3 1
1.1
0.7
0.8
Percentage change - 12 months average
2.4
2.3
2.2
2.1 2
1.8
HICP all items 2014M1
Link to HICP - all items
CPI Consumer Price Index
HICP Harmonised index of consumer prices
Why might month / month-1 data be misleading?
Why might month / month-12 data be misleading?

28. HICP ‘Core inflation’ to 2014M1
geo\time
2013M03
2013M04
2013M05
2013M06
2013M07
2013M08
2013M09
2013M10
2013M11
2013M12
2014M01
M02e
M/M-1
EU (28 countries)
0.2
0.0
-0.6
0.6
0.1
-1.4
Euro area (18 countries)
-0.9
0.7
0.3
-1.7
M/M-12
1.6
1.7
1.5
1.3
0.9
1.0
1.2
1.1
0.8
Percentage change - 12 months average
1.4
HICP ‘Overall index excluding energy, food, alcohol and tobacco’ 2014M1
Link to HICP - all items
TOT_X_NRG_FOOD Overall index excluding energy, food, alcohol and tobacco

30. Financial Times 28/02/2014 Eurozone inflation firm at 0.8% in February
By Claire Jones in Frankfurt and Guy Dinmore in Rome
Eurozone inflation has held firm ahead of next week’s crucial European Central Bank policy meeting, slightly easing pressure on the ECB to tackle falling inflation…
Eurostat, the EU’s statistics bureau, reported on Friday that inflation remained stable at 0.8 per cent in February… Core inflation, which excludes more volatile items such as food and energy prices, rose to 1 per cent, surprising economists, who had expected it to remain at 0.8 per cent.

31. Comments
Distinction press release / commentary: increase in ‘core inflation’
Look at this again under disaggregation
Both look at annual data, not monthly data, which shows a different path
Why is annual data the focus of attention?
Why might annual data be misleading?
What is the risk in imposing a trend on monthly data?

32. Financial Times – Martin Wolf
The spectre of Eurozone deflation, March 11, 2014
The European Central Bank is failing to hit its own target for price stability. … But the ECB has been far less successful in securing price stability… Its aim is to achieve inflation “below, but close to, 2 per cent over the medium term”. Yet in the year to February 2014, headline inflation was 0.8 per cent. This is hardly close to 2 per cent…. Deflation is absent: only three countries have negative inflation and only a fifth of items in the consumer price index have fallen in price. 

33. Avoid contentious analysis
Do not refer to external data or other causes
Martin Wolf again:
… This low inflation has, as is to be expected, coincided with weak demand. In the fourth quarter of last year, eurozone real demand was 5 per cent below levels in the first quarter of In Spain, real demand fell 16 per cent. In Italy, it fell 12 per cent. Even in Germany, real demand stagnated from the second quarter of 2011: this is no locomotive. The failure to offset this has made recovery of crisis-hit economies more difficult, lowered investment and created long-term unemployment. 
He can do this, statisticians cannot

34. The more that data is disaggregated, the less accurate it becomes

35. Disaggregated analysis
Examine top level disaggregation for range of outcomes
Look at contribution of each component to aggregate result
Small components may have higher variability than large components
And may be able to be safely ignored
Do the same for geographical or alternative classification

36. HICP, Euro-18, m/m changes 0.1 -0.5 0.5 -0.1 0.4 -1.1 0.6 -0.4 0.7 0.9
COICOP/TIME
EU-28
Euro-18
2013M05
2013M06
2013M07
2013M08
2013M09
2013M10
2013M11
2013M12
2014M01
All-items HICP
0.1
-0.5
0.5
-0.1
0.4
-1.1
Food & non-alcohl bevergs
158.21
157.13
0.6
-0.4
0.7
Alcoholic bevrgs, tobcco …
45.57
40.44
0.9
0.3
0.2
0.0
0.8
Clothing & footwear
62.12
63.36
-1.2
-13.8
14.5
2.0
-0.2
-0.9
-15.2
Hsing, watr, electrc, gas, othr fuels
158.58
163.19
Furnishings, etc
63.43
65.97
-0.8
-0.7
Health
41.99
43.64
Transport
148.51
152.21
1.0
Communications
31.46
30.58
-0.3
Recreation and culture
100.50
94.78
1.9
-1.8
1.5
-2.5
Education
12.41
10.47
-0.6
Restaurants and hotels
89.83
91.38
Misc. goods & services
87.38
86.85
What is striking about this data?
What is the consequence for aggregate inflation if the high magnitude data are a mistake?

37. Inflation m/m-1 for Member States
GEO/TIME
2013M05
2013M06
2013M07
2013M08
2013M09
2013M10
2013M11
2013M12
2014M01
EU 28
0.1
0.0
-0.4
0.4
-0.1
0.3
-0.9
Euro 18
-0.5
0.5
-1.1
Belgium
-1.3
1.6
-0.2
-1.9
Bulgaria
-0.3
0.2
Czech Republic
Denmark
Germany
-0.7
Estonia
0.6
Ireland
-0.6
Greece
-1.6
-1.7
2.5
0.8
Spain
-1.8
France
Croatia
Italy
1.8
-2.1
Cyprus
Latvia
-1.0
0.7
Lithuania
Luxembourg
1.4
Hungary
-0.8
Malta
1.1
-2.2
-1.5
Netherlands
Austria
1.0
Poland
Portugal
-1.4
Romania
Slovenia
Slovakia
Finland
Sweden
-1.2
United Kingdom
Graph in next session

38. Trends and changes
Fitting trend lines & moving averages to data helps understanding of data
One observation does not usually mean an end to a trend
Even if a new secular trend is established, cycles (seasonal, business cycle) will often overwhelm
Basically, very unlikely to identify a new cycle without applying auto-regression methods

41. Volatility and accuracy
Provide sampling errors for survey-based statistics or explain why this is not done.
Accuracy measures, such as flash estimate accuracy should be statistically based
Maybe R2 ?
Possible data issues to be treated diplomatically
‘a large but potentially reversible change in the price of clothing and footwear’

42. Summary of analysis Aggregate data changes are more important
Examine data over a reasonable period
Period to period changes may be ephemeral
Check that the base period data is not an outlier
Examine data trends in different ways
Use moving averages, fit different trend lines
Look at disaggregated data changes, sceptically
By classification, top level ; geographically
Comprehensive coverage of changes

43. Exercise 4:
What is the figure and the relevant change for the most recent release of your aggregate data?
What is the time period most suitable for discussion – see press release
Is a base-period problem a relevant issue? Otherwise, what are the periods appropriate for trend analysis?
What disaggregation of the data would you look at?
Can you identify the main story?

44. Manage your pages! Management of a wiki is inherently difficult
The organising principle of Statistics Explained is that the technical unit is in charge of their pages
The main identification of pages by Categories is not heirarchical and therefore can be duplicated
The heirarchical Statistical themes page is viewed as secondary and incomplete
See the following links:
Inflation in the euro area
Consumer prices
Annual inflation and its main components
Each page is 'owned' by the statistical unit responsible for the topic. In case of cooperation between units on a subject, a responsible unit is identified after consultation.
Please refer to Wieland presentation:
Numerous overlapping pages exist in Wikipedia – maintaining a coherent structure is very difficult

45. Structure of a longer report: Sustainable development in the European Union - Key messages
Executive summary (19 pages)
Key trends in main sustainable development indicators since Is the EU moving towards sustainable development?
Impacts of the global economic and financial crisis on the key trends
Introduction: The EU set of Sustainable Development Indicators (SDIs)
Analogous to Context and Data Sources

46. Structure of Sustainable development in the EU - 2
Subsequent chapters report data developments (Analogous to Main Statistical Findings):
Socioeconomic development
Sustainable consumption and production
Social inclusion
Demographic changes
Public health
Climate change and energy
Sustainable transport
Natural resources
Global partnership
Good governance

47. Readability Writing should be clear to non-specialists
Short words and sentences, do not use jargon
Technical, statistical terms should be explained in text or in footnotes
If necessary, in a specialised panel or (if nothing else possible) its own page
Refer to Eurostat’s style guides and course Writing with impact
Use reading ease indicators as guides

48. Reading ease indicators in Microsoft Word
File / Options / Proofing
 Show readability statistics / OK
Reading level ideally about that of a 16 year old
e.g. Flesch reading ease >=45
e.g. Flesch-Kincaid grade level <=11
Statistics Explained page ’Inflation in the Euro area’ scores Flesch 24.6 Flesch-Kinkaid 15.6
Alternative reading ease indicators exist
Wikipedia in its article describes issues well

49. Thank you
CONTRACTOR IS ACTING UNDER A FRAMEWORK CONTRACT CONCLUDED WITH THE COMMISSION