Physical inactivity is a major health risk worldwide. Observational studies suggest that higher education is positively related to physical activity, but it is not clear whether this relationship constitutes a causal effect. Using participants (N = 1651) drawn from the Cardiovascular Risk in Young Finns Study linked to nationwide administrative data from Statistics Finland, this study examined whether educational attainment, measured by years of education, is related to adulthood physical activity in terms of overall physical activity, weekly hours of intensive activity, total steps per day, and aerobic steps per day. We employed ordinary least squares (OLS) models and extended the analysis using an instrumental variables approach (Mendelian randomization, MR) with a genetic risk score as an instrument for years of education. Based on the MR results, it was found that years of education is positively related to physical activity. On average, one additional year of education leads to a 0.62‐unit higher overall physical activity (P < .01), 0.26 more hours of weekly intensive activity (P < .05), 560 more steps per day (P < .10), and 390 more aerobic steps per day (P < .09). The findings indicate that education may be a factor leading to higher leisure‐time physical activity and thus promoting global health.

This study examined the relationship between income and physical activity by using three measures to illustrate daily physical activity: the self-reported physical activity index for leisure-time physical activity, pedometer-based total steps for overall daily physical activity, and pedometer-based aerobic steps that reflect continuous steps for more than 10 min at a time. The study population consisted of 753 adults from Finland (mean age 41.7 years; 64% women) who participated in 2011 in the follow-up of the ongoing Young Finns study. Ordinary least squares models were used to evaluate the associations between income and physical activity. The consistency of the results was explored by using register-based income information from Statistics Finland, employing the instrumental variable approach, and dividing the pedometer-based physical activity according to weekdays and weekend days. The results indicated that higher income was associated with higher self-reported physical activity for both genders. The results were robust to the inclusion of the control variables and the use of register-based income information. However, the pedometer-based results were gender-specific and depended on the measurement day (weekday vs. weekend day). In more detail, the association was positive for women and negative or non-existing for men. According to the measurement day, among women, income was positively associated with aerobic steps despite the measurement day and with totals steps measured on the weekend. Among men, income was negatively associated with aerobic steps measured on weekdays. The results indicate that there is an association between income and physical activity, but the association is gender-specific and depends on the measurement type of physical activity.

This paper contributes to the literature on the labor market consequences of obesity by using a novel instrument: genetic risk score, which reflects the predisposition to higher body mass index (BMI) across many genetic loci. We estimate instrumental variable models of the effect of BMI on labor market outcomes using Finnish data that have many strengths, for example, BMI that is measured rather than self‐reported, and data on earnings and social income transfers that are from administrative tax records and are thus free of the problems associated with nonresponse, reporting error or top coding. The empirical results are sensitive to whether we use a narrower or broader genetic risk score, and to model specification. For example, models using the narrower genetic risk score as an instrument imply that a one‐unit increase in BMI is associated with 6.9% lower wages, 1.8% fewer years employed, and a 3 percentage point higher probability of receiving any social income transfers. However, when we use a newer, broader genetic risk score, we cannot reject the null hypothesis of no effect. Future research using genetic risk scores should examine the sensitivity of their results to the risk score used.