Abstract
A measure of entrepreneur performance is important to identify current and future successful ventures, to further our understanding of the entrepreneurial process and to guide public policies to improve the success rate of start-ups. We propose a single value index of entrepreneur performance that ranges from one to zero, that is predicated on multiple inputs, and that mitigates the impact of outliers. The index is calculated for firms from the Kauffman Foundation Firm Survey for years 2004 through 2009. The average value of the index increases over time as firms either improve their performance or exit the data set due to failure. Survival analysis shows that the index serves as a leading indicator of success, although many additional factors are important.
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Notes
For a review of frontier methods, see Fried et al. (2008).
It might appear to be inconsistent to use a frontier approach that is justified by the special information content of outlying observations and to combine this with order-m methodology that injects an element of noise, thereby mitigating the impact of outlying observations. This is the same view of the world that underlies a stochastic frontier. Outlying observations are important, but all observations include an element of noise.
This straightforward interpretation applies to traditional FDH scores and not order-m FDH since these scores can be greater than one and are more akin to super efficiency scores.
If a specific firm is doing extremely well and is randomly included in the random selection of 250 firms (order-m of 250) then it might only have an index of 1.00. If it is not included in the random selected 250 firms it might produce an index score greater than 1.00.
The routines stcox and streg in STATA were used to estimate the Cox and Weibull regressions, respectively.
We recognize that there is a potential errors in variables problem as a result of including the order-m FDH performance index as an independent variable in the survival models. We do not attempt to measure or adjust for this bias, but we did calculate correlations coefficients for all of the x variables. Most were not strongly correlated.
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Acknowledgments
We are grateful to the Ewing Marion Kauffman Foundation for providing financial support and access to the proprietary version of the Kauffman Firm Survey. Alicia Robb guided us in understanding a very large and intimidating data set. Tim Mulcahy was our mentor for working in the secure space of the NORC Enclave. We thank Leopold Simar for providing the Matlab routines to calculate order-m efficiency scores. Robin Sickles provided valuable suggestions and guidance. And we thank two excellent referees.
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Fried, H.O., Tauer, L.W. An entrepreneur performance index. J Prod Anal 44, 69–77 (2015). https://doi.org/10.1007/s11123-015-0436-0
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DOI: https://doi.org/10.1007/s11123-015-0436-0