Finfacts has often reported on research which shows that young firms up to 5 years old are typically responsible for most net job creation in an economy. This research usually based on US data and published by the Kauffman Foundation, an entrepreneurship think-tank, has now been supplemented by a useful working paper from the Organisation for Economic Cooperation and Development (OECD).
Bart Clarysse, professor of entrepreneurship, at Imperial College London Business School, said in 2009 on most tech startups: "They don't become the new Microsoft. They just stay micro"
Daniel Isenberg, a former entrepreneur who created the entrepreneurship ecosystem project at Babson Executive Education commented last year:
Across a large sample of OECD and emerging countries, young — rather than small firms as a whole — are net job creators, even during the Great Recession.
Using data collected for the DynEmp database covering 17 countries and papers on the US and Irish experience, the OECD paper points to substantial differences across countries in the extent to which new firms can grow if they prove to be successful — eventually increasing the overall productivity of the economy.
Countries may have very high entry rates but low average post-entry growth, others might have high entry rates but low survival probabilities or, vice versa, a low entry rate but high post-entry growth.
The authors say these different elements are not necessarily positively correlated, "and while all contribute to explaining differences in the extent to which startups contribute to aggregate job creation in the economy the extent to which they do so varies across countries. For instance, in Belgium the start-up 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 start-up rate is high but average post-entry growth is much lower."
The data show that the survival rate is on average equal to just above 60% after three years from entry, to about 50% after five years, and to just over 40% after seven years. "Furthermore, it appears as a striking regularity across many countries that the probability of exiting is highest when businesses are two years old, and decreases (linearly) beyond that age."
The large majority of surviving startups do not grow.
The paper highlights that the tiny proportion of small transformational entrepreneurs’ startups that do grow — around 3% on average across all countries — creates a disproportionate amount of jobs, from 21% (in Netherlands) to 52% (in Sweden) of the total job creation by micro startups.
"This point is extremely relevant for policy making. Without taking this disproportionate contribution of scaleups into account, there is indeed the danger of overlooking the critical importance of young firms as the engine of job creation in light of the fact that the large majority of them do not grow, or grow very slowly," the authors say.
The authors say that Figure 2 (chart above) summarises "the main synthetic indicator on the average number of jobs created by surviving start-ups over a three years period. The measure is calculated as the ratio between total employment of entrants at the end of a three year period (on average for three different cohorts, born in 2001, 2004, and 2007, respectively) and overall employment in the country at the beginning of the three year period. This synthetic measure is henceforth referred to as “normalized net job creation” by surviving entrants. Country heterogeneity emerges when observing the patterns of normalised net job creation by surviving entrants. A handful of economies — namely Turkey, Brazil, Sweden, New Zealand and, to a lesser extent, Spain and Hungary — are characterized by a higher normalised net job creation. In these countries, net job creation by entrants that survive at least three years represents up to 7% of overall employment; i.e. for every existing 100 jobs in the economy in any given year, the startups which are born in that year will add 7 new jobs within the following three years."
The DynEmp (Dynamics of Employment) database provides a dataset which covers around 10 years for most countries, starting from the early 2000s’ until the 2011 or 2012. The DynEmp is a harmonised, 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 a project of the OECD Directorate for Science, Technology and Innovation. Countries included in the paper's dataset were:
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
Jonathan Ortmans, president of the US Public Forum Institute summarised the findings for the Kauffman Foundation. He said that 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 emphasised 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."
Jonathan Ortmans added that 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."
Ortmans concluded: "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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