In 2012, the Kauffman Foundation partnered with LegalZoom to conduct a survey series of new business owners in an effort to establish a broader range of metrics than are available in more traditional data sets. Q1-Q4 saw confidence surveys that helped shine light on entrepreneurs’ particular take on consumer confidence and overall economic health.
At year’s end, we followed up on these confidence indexes with a final survey of 1,431 newly incorporated businesses focused on identifying founder and venture characteristics. The official report already offers a good summary of the important details gleaned from the data, so let’s instead explore some of the data’s limitations and what they mean for this survey and entrepreneurship statistics beyond it.
The scope of the survey starts coming into focus with a breakdown of the financing. When respondents were asked where they obtained startup capital, personal funding dominated all other external sources (83% of ventures used personal funding; 59% used it as the sole source). Investors backed just 7% of ventures in the sample, and only 5% of owners secured bank loans. With most funding coming out-of-pocket, we should anticipate these businesses to be relatively small, and the data back up this expectation. 82% of companies in the sample had revenues between 0 and $49k, and 70% had no employees other than the owner(s).
Given our sample’s slender financial profile, it is unsurprising that the top business type among respondents was “Consulting” (followed by “Other” and “Service: Other Services”). First, we should account for the possibility of selection bias here – self-employed consultants are likely to have more time and therefore greater inclination to answer a survey than do owners with responsibilities to multiple employees and complex revenue streams. Nonetheless, we should not expect selection bias to account for the entirety of such a heavy weighting toward this type of business activity.
Accordingly, we should be wary not only of our own data’s limitations, but also of any statistics on small business which do not specify revenue or employment. For example, should a city or state boast responsibility for having “x” new businesses started in 2012, it’s worth the deeper dive to see how many of those will truly be impact businesses that bring jobs and money to the region. The research of Davis et al. demonstrates that nonemployers can and do migrate to the employer-universe, becoming a substantial source of revenue and growth in the period surrounding the transition. Nonetheless, only 3% of nonemployers ever make this transition, meaning that our sample’s economic footprint is bound to be very limited given the size of the survey. Nonemployers of all stripes play a role in the economy, but policymakers will want to focus primarily on those with growth aspirations, making “new businesses” with no further distinction a crude, weak metric.
Finally, perhaps more interesting than any information contained in the survey was the information that couldn’t be included. Among the respondents, 91% worked on their business for more than a month before incorporating. 60% worked on it for more than 6 months, and 35% did so for over a year. This is the black box of entrepreneurial studies, and the biggest problem facing the field: how do we obtain information about businesses in that crucial planning and preparation period? Could we only obtain information about that window of time with retrospective surveys, and if so would the data be tainted by the imperfections of memory? If we were able to contact entrepreneurs as they toiled through the development phase, would the very act of observation change their actions and invalidate the experiment? We may be no closer to answering these questions today, but our respondents have soundly reaffirmed the need to try.
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