1. How does early statistical output become late statistical input?
CESS 2016 – Session C3 "Timely estimates of economic indicators"
Ingo Kuhnert
European Commission
DG Economic and Financial Affairs

2. How statisticians see timeliness
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3. How data users see timeliness
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4. Outline 1. Timeliness and other key quality features 2. Timing
3. A case study
4. How to adapt
5. Conclusions

5. Key quality features of statistics
Timeliness
the length of time between data availability and the phenomenon they describe
but also
Punctuality
the time lag between the actual delivery of data and the scheduled release target date
Accuracy
the degree of closeness of estimates to the true values they were intended to measure
Reliability
the closeness of the initial estimated value to the subsequent estimated value
Relevance
the degree to which statistical information meets user needs (includes Completeness)
Definitions taken from ESS quality reporting guidelines, shortened
Relevance here is a narrower concept

6. Key quality features of statistics II
How does timeliness relate to:
Frequency: ambivalent – higher frequency means earlier data, but shorter reporting periods make data look old sooner
Reliability: more or less badly. Also, if timeliness is improved via an additional early release, there is more revision slots
Relevance: positive but asymmetric (too late = irrelevant). If earlier data is less complete, part of the gain vanishes.
(Accessibility and Clarity)
Issue: if timeliness is such that another full reference period has passed before release (t+4 months is already a quarter ahead)
ANA as sum of 4Q issues.
Relevance in a wider sense than the ESS quality report interpretation

7. Key quality features of statistics III
Timeliness improvements will not necessarily result in appropriate increase in user satisfaction because many users have a large bundle of needs. Remove the most pressing shortcoming and others that were curtailed will come to the front.
On the contrary, they can trigger additional user needs in particular as regards completeness and reliability.
Competing risks analogy

8. Timeliness of quarterly (EU) GDP
ESA 95 original (1999): t+4 months (legal)
Flash GDP growth (2003): t+45 days
ESA 95 amended (2007): t+70 days (legal)
ESA (2014): t+2 months (legal)
Preliminary flash (2016): t+30 days

9. Timing: What's output to you is input to me
Gauging the economy needs a long production chain: users of statistics may well appear as producers of statistics to their respective clients.
Institutional boundaries can over-emphasize the user – producer antagonism.
Business Statistics
National Accounts
Economic Forecasts
Economic Governance

10. Timing: a simple scheme
Inputs
Process
Outputs
When does the last necessary input arrive?
What is the minimum time needed for production?
When is the first compulsory output committed?

11. Timing: producers and users
Traditionally, producers of statistics could wait until they have all necessary source data, add a processing time that allows quality checking and a safety margin, and derive a release date.
Users who need a single data input are in a comparable position, but users who need data from several statistical domains have to look for a lull in statistical releases to insert their process.
Even users who concentrate on one input cannot do the same if that input is subject to revisions.

12. Timing: Interference input arriving after production cannot be helped
Inputs
Process
Outputs
input arriving after production cannot be helped
input arriving during production bad

13. A case study: European Semester
Implementation of the EU’s economic governance is organised in an annual cycle – the European Semester – in which the European Commission, Council, Parliament and Member States interact.
As part of the European Semester, the European Commission (COM) analyses the fiscal and structural reform policies of every Member State, provides recommendations, and monitors their implementation.
COM produces economic forecasts aligned in time with key milestones in the European Semester process:
November: COM publishes the Annual Growth Survey (AGS), setting out proposals for EU priorities in the coming year
May: COM proposes country specific recommendations (CSRs) on economic and budgetary policies

14. A case study: DG ECFIN economic forecasts (Spring forecast 2016)
Temporal coverage: 2016 & 2017 (2016Q1 to 2017Q4)
Geographical coverage: EU/EA, EU member States, US, JP
Frequency: annual, except for GDP and HICP (quarterly)
Variables coverage: GDP and components, deflators, HICP, population, employment, general government finance, current account balance, exchange rates
Inputs: historic data for variables covered from ESTAT, national sources, commercial suppliers, monthly data, business and consumer surveys, …
Published: 03/05/2016
Also cyclically adjusted GG balance, productivity, output gap, … , primary commodity prices

15. ESTAT releases end April 2016
08/03/ GDP and main aggregates (2015Q4) …
19/04/ monthly BoP (Feb 2016)
21/04/ EDP spring notification release (2015)
22/04/ QGFS release (2015Q4)
29/04/ preliminary GDP flash release (2016Q1)
29/04/ Unemployment (March 2016)
29/04/ Inflation (HICP) (April 2016)
13/05/ GDP flash release (2016Q1)

16. ECFIN forecast calendar (SF 2016)
02/03/ Kick-off seminar …
22/04/ Cut-off (12:00)
22/04/ Trade consistency exercise (Fri)
25/04/ Final text contributions (Mon)
29/04/ Final text merge (Fri)
02/05/ Printing
03/05/ Release ( )
18/05/ adoption of CSR package

17. A case study: Timing Blue = QNA (Q-2) Yellow = AGS/CSR production
Inputs
Process
Outputs
Blue = QNA (Q-2)
Red = EDP (A-1)
Dot = GDP t+30 (Q-1)
Dash = GDP t+45 (Q-1)
Yellow = AGS/CSR production

18. How to adapt to time pressure
Advance inputs (most comfortable solution, pass pressure and blame down the chain)
Replace inputs (difficult, loss of quality and/or need for process changes)
Shorten your own production time (uncomfortable but good idea if there is slack)
Relax outputs timeliness requirements (usually very little influence)

19. How to adapt: Solution (v1)
Inputs
Process
Outputs
All timing unchanged, but an extra reconciliation step added in the process

20. How to adapt: Solution (v2)
Inputs
Process
Outputs
Process prolonged and segmented. The preliminary GDP flash now is the last input.
In parallel, European semester-related steps have been slightly postponed.

21. Conclusions
Both users and producers have to make an effort to understand the timing constraints on the other side(s) better
Education of users about the trade-off between timeliness and other quality aspects
Better coordination of statistical production processes across & within domains
Harmonised release and revision policies
Further acceleration of statistical production
Adequate resources and political support for statistical production if a strong policy need has been identified