Evaluating entrepreneurship training: How important are field experiments for estimating impacts?

Governments and donors around the world spend billions of dollars subsidizing entrepreneurship training programs. Unbiased evaluation estimates are paramount to understanding whether subsidies and investments in these programs are justified. The goal of this paper is to compare nonexperimental to experimental methods for evaluating the effectiveness of entrepreneurship training programs that provide training in management, marketing, accounting, legal, and other aspects of starting and running businesses. For the comparison, I take advantage of an unprecedented setting in which experimental and nonexperimental estimates are derived from the same underlying population of study participants. The Growing America through Entrepreneurship field experiment provides a uniquely large sample allowing for a separate nonexperimental analysis using only the control group. Experimental estimates indicate null effects of entrepreneurship training on business outcomes: business ownership, sales, and employment. In contrast, nonexperimental estimates using an extremely rich set of controls, including typically unobservable characteristics, such as previous family business experience, credit problems, and personality traits, find large, positive, and statistically significant effects. The nonexperimental estimates range from 21 to 22 percentage points (pp) for business ownership, $1300–2000 for average monthly sales, and 4–6 pp for hiring any employees at 1.5‐ and 5‐year follow‐ups. Nearest‐neighbor and propensity‐score matching models using detailed individual characteristics provide similarly large, positive, and statistically significant effects of entrepreneurship training on business outcomes. The findings have implications for choosing evaluation methods of government programs and provide evidence of positive selection bias which has more general implications on the scalability of entrepreneurship training programs to broader populations. [ABSTRACT FROM AUTHOR]