Furthermore, the health economics literature suggests that the role of the strongly depends BMI on gender: for instance, while found for females, there is no clear association between wages and for males (Baum, 2004; Cawley, 2004). Therefore, to fully disentangle the links between BMI health variables and risk preferences we present two distinct sets of estimations for young men (Tables 4-5) and women (Tables 6-7) in our sample. As we pool all experimental responses together, results are based on a minimum of 2,490 and 1,910 observations, respectively. The gender-specific structural estimations suggest that females in our sample are slightly more risk averse, having an estimated CRRA coefficient compared to for males. For risk preferences do not appear to significantly vary across different ethnicity and young men socio-economic background, as reflected by the income variable (Model in Tables Budget I-XIV 4-5). Nevertheless in most specifications the parent education level is significant with a negative sign. If one education as a proxy of the income and background interprets parents’ household’s (and given that students is a very crude proxy for income) this result is in line with what Budget usually found by the literature. [Insert Tables 4 and 5 here] Concerning health habits, (dummy equal to 1 if the is above 30) appears ObeseD BMI significantly associated with the degree of risk aversion among young men in our sample (Models Table 4): male subjects with a above 30 appear to be more risk-seeking. II and III, BMI
Furthermore, the health economics literature suggests that the role of the BMI strongly depends on gender: for instance, while found for females, there is no clear association between wages and BMI for males (Baum, 2004; Cawley, 2004). Therefore, to fully disentangle the links between health variables and risk preferences we present two distinct sets of estimations for young men (Tables 4-5) and women (Tables 6-7) in our sample. As we pool all experimental responses together, results are based on a minimum of 2,490 and 1,910 observations, respectively. The gender-specific structural estimations suggest that females in our sample are slightly more risk averse, having an estimated CRRA coefficient compared to for males. For young men risk preferences do not appear to significantly vary across different ethnicity and socio-economic background, as reflected by the income variable Budget (Model I-XIV in Tables 4-5). Nevertheless in most specifications the parent education level is significant with a negative sign. If one interprets parents’ education as a proxy of the household’s income and background (and given that students Budget is a very crude proxy for income) this result is in line with what usually found by the literature. [Insert Tables 4 and 5 here] Concerning health habits, ObeseD (dummy equal to 1 if the BMI is above 30) appears significantly associated with the degree of risk aversion among young men in our sample (Models II and III, Table 4): male subjects with a BMI above 30 appear to be more risk-seeking.