Statistical discrimination occurs when distinctions between demographic groups are made on the basis of real or imagined statistical distinctions between the groups. While such discrimination is legal in some cases (e.g., insurance markets), it is illegal and/or controversial in others (e.g., racial profiling and gender-based labor market discrimination). “First-moment” statistical discrimination occurs when, for example, female workers are offered lower wages because females are perceived to be less productive, on average, than male workers. “Second-moment” discrimination would occur when risk-averse employers offer female workers lower wages based not on lower average productivity but on a higher variance in their productivity. This paper reports results from controlled laboratory experiments designed to study second-moment statistical discrimination in a labor market setting. Since decision-makers may not view risk in the same way as economists or statisticians (i.e., risk=variance of distribution), we also examine two possible alternative measures of risk: the support of the distribution, and the probability of earning less than the expected (maximum) profits for the employer. Our results indicate that individuals do respond to these alternative measures of risk, and employers made statistically discriminatory wage offers consistent with loss-aversion.