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family member's business, had previous work experience in a managerial capacity, and

worked in a business providing similar goods and services.

Race and ethnicity are important determinants of small business outcomes (see

Fairlie and Robb 2003 for a more detailed analysis). Asian-owned businesses generally

have better outcomes than white-owned businesses. After controlling for numerous

owner and business characteristics, black-owned businesses continue to lag behind white-

owned businesses. In all specifications except the closure probability equation, the

coefficient estimate on the black-owned business dummy variable is large, positive and

statistically significant. In the closure probability equation, the coefficient estimate is

positive, but statistically insignificant. The results are more mixed for Latino-owned

firms. They have a lower probability of having large profits, but have a higher

probability of hiring employees than white-owned firms. The coefficient estimates in the

other two specifications are statistically insignificant.

Similar to previous studies, we find that business outcomes are positively

associated with the education level of the business owner.14 For example, businesses

with college-educated owners have a 0.055 lower probability of closure, a 0.113 higher

probability of having large profits, a 0.060 higher probability of having employees, and

have approximately 25 percent higher sales on average than businesses with owners who

did not graduate from high school.15 Female-owned businesses are less successful and

are smaller on average than are male-owned businesses. Firms located in urban areas are

14 For example, using the 1982 CBO, Bates (1990b) finds that small business failures generally decrease with the education level of the owner. Similarly, Robb (2000) found increases in e d u c a t i o n t o b e p o s i t i v e l y a s s o c i a t e d w i t h b u s i n e s s s u r v i v a l . 1 5 The implied effects on the probability of closure, large profits, and employment are

approximated by multiplying the coefficient estimate from the logit model by p(1 p) , where p is the mean of the dependent variable.


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