Using nationally representative micro panel data on flu incidence from the Medical Expenditure Panel Survey in the United States, we show that employed individuals are on average 35.3% more likely to be infected with the flu virus. Our results are robust to individual characteristics including vaccinations, health insurance and individual fixed effects. Within the employed, we find significant differences in flu incidence by occupation (e.g., sales occupations show 40.5% higher probability of infection than farmers) and by industry (e.g., education, health and social services show 52.2% higher probability of infection than mining). Further, we show that the interaction between occupations and industries is important to understand contagion. Indeed, cross-industry differences in flu incidence cannot be fully explained by differences in the within-industry occupation structure. As a potential mechanism for contagion, we study how flu incidence varies with the extent of human contact interaction at work—with an occupation-industry-specific score that we construct based on O’NET occupational characteristics. We find that the higher the human contact at work, the greater are the odds of infection. Our results are larger in years of high aggregate flu incidence and robust to firm size, a number of jobs and hours worked.

我们使用来自美国医疗支出小组调查的具有全国代表性的关于流感发病率的微面板数据，显示受雇人员感染流感病毒的可能性平均高出 35.3%。我们的结果对包括疫苗接种、健康保险和个人固定效应在内的个人特征是稳健的。在受雇者中，我们发现不同职业（例如，销售职业显示感染概率比农民高 40.5%）和行业（例如，教育、卫生和社会服务显示感染概率比采矿职业高 52.2%）的流感发病率存在显着差异. 此外，我们表明职业和行业之间的相互作用对于理解传染很重要。确实，流感发病率的跨行业差异不能完全用行业内职业结构的差异来解释。作为一种潜在的传染机制，我们研究了流感发病率如何随着工作中人类接触互动的程度而变化——我们根据 O'NET 的职业特征构建了职业行业特定的分数。我们发现工作中的人际接触越多，感染的几率就越大。我们的结果在流感总发病率高的年份中更大，并且对公司规模、工作数量和工作时间也很稳健。我们发现工作中的人际接触越多，感染的几率就越大。我们的结果在流感总发病率高的年份中更大，并且对公司规模、工作数量和工作时间也很稳健。我们发现工作中的人际接触越多，感染的几率就越大。我们的结果在流感总发病率高的年份中更大，并且对公司规模、工作数量和工作时间也很稳健。