Table 1 reports coe¢ cient estimates from systems of seemingly unrelated regressions, where each dependent variable is a dummy for working at baseline in a one-digit industry and the right hand side is a dummy for the father or mother being in that industry and a dummy for the parent working, in addition to race, age, and education xed e¤ects1. So as to not multiple count individuals, I use the fraction of working years spent in an industry or occupation for those who work in multiple industries or occupations. Sonsindustries appear highly correlated with their fatherswhile daughtersindustries are highly correlated with their mothers. While father-daughter and mother-son correlations are occasionally signi cant for an individual industry, they are always small in magnitude and are jointly insigni cant. A natural extension is to consider occupational correlations as well as industries. Table 2 reports coe¢ cient estimates for occupations from seemingly unrelated regressions, following the methodology used for industries. The di¤erence between the occupation and industry correlations are striking. Industries are correlated along gender lines, with most industries being associated with an 7-12% increase in likelihood of employment in that industry for children if their parents work in that industry and happen to be the same gender. Occupational correlations also are largest within mothers-daughter pairs, and look quite similar across the other three groups. Once again, they are often individually signi cant for a given occupation, but a joint test reveals that parents occupations are not correlated with childrens across the sample in any of the 4 parent-child pairs. 1Results are qualitatively una¤ected by omitting these demographic controls.