Deconstructing Job Creation from Startups
Last month, the Organization for Economic Cooperation and Development (OECD) released a thoughtful new working paper, "Cross-country evidence on start-up dynamics", that showed patterns across countries that are similar to those found for the United States. The report offers important insights for researchers and policy makers fine-tuning how to measure the dynamics of new firm formation.
In the paper, co-authors Flavio Calvino, Chiara Criscuolo and Carlo Menon explain that the contribution of new firms in terms of new jobs to the existing workforce can be expressed as a combination of four different elements:
- The startup rate: measured as the number of entrants relative to the country’s total employment.
- The average size of firms at point of entry: calculated as the number of units that survive until or beyond the third year of life over the total number of starting units.
- The survival rate: measured as the average number of employees for entrants.
- The average growth rate of survivors: calculated as the final over initial employment ratio of surviving entrants.
The Four Elements Across Economies
The above four factors help explain the extent to which startups contribute to aggregate job creation in the economy. However, even in economies with similar aggregate startup contributions, these four forces interplay in very different ways, especially in startup ratios and average post-entry growth performances.
This means that the extent to which any of these individual forces affect job creation varies across countries. “For instance, in Belgium the startup rate is very low, but the post-entry growth rate of survivors is the highest in the sample. Conversely, in New Zealand and Turkey the startup rate is high but average post-entry growth is much lower”, explains the report.
Here are some trends that emerged from the OECD analysis:
- The most homogenous component across countries is the survival rate, which equals to about 60% after three years from entry, to about 50% after five years, and to just over 40% after seven years.
- The first two years of a firm’s activity seem to be crucial in determining the fate of that firm: in most countries the first two years of activity are characterized by a much higher average employment growth rate for those entrants that survive.
- In most countries, the probability of exiting is highest at the age of two, and decreases linearly thereafter.
- When looking at employment growth of surviving businesses, the large majority of surviving micro startups do not grow; however, the tiny proportion of small startups which do grow (around 5%) creates a disproportionate amount of jobs – from 21% of the total job creation in Netherlands to 52% in Sweden. This tiny portion is called the group of “transformational entrepreneurs” in the report.
- The startups of these transformational entrepreneurs that do grow do so at around 3% on average across all countries.
The report used the OECD’s recently collected DynEmp (Dynamics of Employment) database. The dataset covers around 10 years for most countries, starting from the early 2000s’ until the 2011 or 2012. The DynEmp is a harmonized, cross-country, micro-aggregated database on employment dynamics from confidential micro-level data, where the primary sources of firm and establishment data are national business registers.
DynEmp is an ongoing project led by the OECD Directorate for Science, Technology and Innovation. For the paper, countries included in the dataset were:
- Costa Rica
- New Zealand
The dataset is growing to encompass more countries in the future with the support of national delegates and national experts in member and non-member economies.
Consistent Evidence on Age vs. Size
Across the entrepreneurship literature, young firms aged five or less – rather than small firms as a whole – are always, and by a fair amount, net job creators, even during the Great Recession. The OECD itself has previously emphasized that young firms are the engine of job creation using evidence on 17 OECD countries and Brazil.
In Ireland, Martina Lawless of the Economic and Social Research Institute found the same to be true for her country-specific analysis “Age or Size? Contributions to Job Creation”. In the United States, Kauffman research has shown that without startups, net job creation for the American economy would be negative in all but a handful of years. Moreover, data has dispelled the myth that firms bulk up as they age. In fact, gross flows decline as firms age.
The distinction of firm age, as opposed to size, as the driver of job creation has already had many implications for policymakers who are trying to leverage entrepreneurship to address unemployment. However, the decomposition of the net job contribution by entrants into four components – average size at entry, startup ratio, survival rate, and average growth rate – promises to improve the ability of policymakers to fine-tune policy interventions in response to the relative weight of each of these four factors in any specific economy. As the OECD report emphasizes, one size does not fit all.
The data set also itself opens new avenues for policy impact evaluation. Statistics at the country-industry-year level differentiated across entrants and incumbents allows for a greater understanding of the differential impact of policies on entrants versus incumbents.
This approach to deconstructing the net job creation contribution into multiple elements will also be invaluable for the global research community at large. For example, the OECD is a member of the Global Entrepreneurship Research Network (GERN), a global collection of research institutions that collaborate on entrepreneurship research projects - sharing data and methodologies - the OECD Statistics Directorate and UNCTAD for example are collaborating to shed more light on entrepreneurship data infrastructure. As policymakers and researchers across the globe strive for more precise evidence of the impact of job creation efforts through increased rates of new firm formation, analytical papers like “Cross-country evidence on start-up dynamics” are excellent examples of how we can elevate the quality of work in the field.
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