1. ESSnet on linking of micro-data on ICT usage Progress Report Mark Franklin UK Office for National Statistics Cologne: 27 October 2011

2. 2 Agenda Context: –What is the project about? –Where does the project sit in the statistical system? –Building on Feasibility Study Brief overview of project Some project issues Q & A

3. 3 Purpose of project Making better use of data existing in the statistical system: –Produce new policy relevant indicators without the need to collect more data and without increasing the burden on enterprises. –Re-use data for purposes beyond the initial objectives for collecting such data. –Focus on economic impacts of ICT usage. But the methodology can be generalised to a range of policy issues and data sources.

4. 4 Where does the project sit in the statistical system? Project indicators are examples of distributed micro data or meso data Meso data sit between macro and micro forms of data Illustrate by the data-generating processes …

5. 5 Macro: data process Macro indicators (national accounts, trade, inflation, public finances etc) cannot be reproduced purely from survey data. Macro indicators are contingent on national accounts conventions (SNA, ESA), e.g. GFCF asset classes. Macro indicators are rich in structure and consistency (with other indicators, and other countries’ data), but poor in detail. Surveys Admin data Judgements, adding-up constraints etc “Black box” Compilation process Macro Indicators

6. 6 Micro: data process Micro indicators can in principle be reproduced purely from survey data. Micro indicators are contingent on survey design, e.g. E- Commerce survey. Micro datasets are rich in detail, poor in consistency and structure. In particular, cross-country analysis of microdata is difficult. Run survey Clean data, Re-weight etc Published micro Indicators [Some NSIs] Micro dataset available to researchers in safe centre

7. 7 Meso: data process Meso indicators can be reproduced purely from (micro-data versions of) survey data. Design is contingent on survey design, informed by policy relevance, e.g. ICT usage characteristics of firms by quartile of productivity; Cut survey data by industry, size class, whether multinational, young/old etc. Exploits richness of firm-level variation; yet consistent between countries. Survey #1, Country #1 Survey #2, Country #1 Survey #3 …, Country #1 Common data-generating Code, Multiple Countries Meso Indicators, Country #1 Meso Indicators, Country #2 Meso Indicators, Country 3….

8. 8 Example: Should governments subsidise investment in broadband networks? Evidence based policy making – need evidence on relationship between broadband access and firm performance across a group of countries. Could design a new survey to investigate the relationship (Q1:Do you have access to broadband? Q2: What is your growth of turnover/employment? …), but… -Costly -Time consuming -Difficult to co-ordinate across countries -Add to “red tape” burden on survey respondents What’s wrong with using ‘macro’ indicators? –Not the same firms! What’s wrong with using ‘micro’ indicators? –Cannot identify impacts of policy changes from a single country study –Multi-country micro studies are rarer than hens teeth.

9. 9 Builds on 2006-08 Feasibility Study Broader scope: –More participants –Longer runs of annual datasets –New datasets, in particular the Community Innovation Survey Develop and generate meso indicators, and conduct some exploratory analysis of ICT impacts using these indicators Develop a schema for providing access to indicators Explore lessons for survey strategies.

10. 10 Project Overview 15 NSIs. Steering group of 5 NSIs make recommendations to whole group 22 months: December 2010 – October 2012 2 contracted academic partners, plus liaison with other research bodies 7 Workstreams: A.Co-ordination and financial management (ONS) B.Metadata Review (ONS) C.(Lessons for) survey strategies (Stats Norway) D.Impact analysis (CBS) E.Dissemination (ONS) F.Technical infrastructure (Stats Sweden) G.Data dissemination (CBS)

11. 11 Project Issues - 1 Formulation of indicators and data boundaries (workstreams (b) and (d)) –E-commerce variables: a range of different views across the project group over what variables are most relevant –CIS variables: initial set of indicators agreed by analytical steering group, coded by academic contractor, being tested by steering group Cycling through metadata-indicators-analysis is a time-consuming process.

12. 12 Project Issues - 2 Data sharing (workstreams (f) and (g)) –Meso indicators are not micro-data, but are derived from micro-data, and hence subject to disclosure control –Two dimensions to this issue: Internal: Secure FTP platform on which cross-country meso indicators are compiled. Access restricted to analytical steering group, subject to confidentiality agreements. External: Develop a protocol under which the cross- country meso indicators could be made available to outside researchers, and beyond the life of this project.

13. 13 Any questions?

14. Mark Franklin Economic Interpretation Division Office for National Statistics Mark.Franklin@ons.gov.uk +44 (0)1633 455981 This work contains statistical data from ONS which is Crown copyright and reproduced with the permission of the controller of HMSO and Queen's Printer for Scotland. The use of the ONS statistical data in this work does not imply the endorsement of the ONS in relation to the interpretation or analysis of the statistical data. This work uses research datasets which may not exactly reproduce National Statistics aggregates.

15. 15 Blank slide

16. 16 Feasibility study on national survey strategies Workstream C - led by NOR. Objectives –to carry out a feasibility study on redesign of national survey strategies, including a study of the existing practices. –to present strategies for improving data representativeness including their cost-benefit analysis. The study will cover linked datasets provided by the participating NSIs. Components –Analysis of existing surveys and practices to improve representativeness of linked data. –Presentation of the main challenges to data linking. –Ways to improve representativeness of the linked data.

17. 17 Project time line