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

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

Current Position Inventory of 49 Internet data sources from 27 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

Factors Affecting Comparability Reasons why data are not comparable –Genuine variation –Methodological differences

Proportion of businesses with no employees Source: Taken from “Business Demography in Europe, Results for 10 Member States and Norway”, Table 2.5, published by Eurostat in Data for 2001 (except Belgium – 2000)

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

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?

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

United States Data – 4 Sources

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

What Next? Short-term: –Apply framework to data from different countries –Standard methods to adjust for methodological differences? - “Quick wins” –Impact of births – how important are they really for job creation?

What Next? Short-term: –Apply framework to data from different countries –Standard methods to adjust for methodological differences? - “Quick wins” –Impact of births – how important are they really for job creation? Long-term: –What do data users really want? –“Gold-standard” methodology

Thank-you for listening. Any Questions?