1. OECD
OCDE
International Data on Business Start-ups: Factors Affecting Comparability
Steven VALE, OECD / ONS

2. The Issue
Increasing political and academic interest in business demography and entrepreneurship
Accurate measures of business start-ups are important indicators for both
OECD project on international comparability of start-up rates
Funded by the International Consortium for Dynamic Entrepreneurship Benchmarking, led by FORA

3. Current Position
Inventory of 48 Internet data sources from 26 countries
Statistical Business Register is the most common source
Examination of metadata to try to understand the differences in sources
Typology of factors affecting comparability
Tested on countries with multiple sources

4. Factors Affecting Comparability
Reasons why data are not comparable
Genuine variation
Methodological differences
Factors provide a framework to help understand methodological differences
Start-up rates = New Businesses Population
Numerator and denominator factors

5. Numerator Factors Source – register, census or survey?
Units – enterprise, establishment,...?
Scope – inclusions / exclusions?
Threshold – small units?
Time – when to measure?
Purity – births or other events?

6. Denominator Factors Population – businesses or people?
Coverage – inclusions / exclusions?
Temporal basis – “point in time” or “live during period”?

7. United States Data – 4 Sources

10. Reasons for Differences
If data are for one country, differences must be entirely methodological
Most important factors:
Scope (numerator)
Coverage (denominator)
Source (numerator)
Purity (numerator)
Temporal basis (denominator)

11. Conclusions
The charts clearly show why start-up rates need to be broken down into their components
Each component is affected by a number of factors
A framework to systematically assess these factors helps to make better informed and more reliable comparisons

12. What Next? Short-term: Long-term:
Apply framework to data from different countries
Standard conversion methods / ratios to adjust for methodological differences?
Look for “quick wins” – improving comparability for negligible cost
Long-term:
What do data users really want?
“Gold-standard” methodology as a target to work towards

13. Thank-you for listening.
Any Questions?