overweight or obese was defined by a dummy variable equal to 1 for BMI levels above 30. Similar results were obtained when changing the ‘cut-off’ thresholds to categorise smoking and heavy drinking behaviours. In their interpretation, the authors suggest that “some caution is in order” as their “results are sensitive to how the behaviour is defined”, and observe that “additional exploration of how risk behaviours are defined may be worthwhile in future studies” (p.1270). Contributing along this line, here we use a richer set of health indicators in the attempt to obtain estimates less sensitive to changes in the categorization of risky habits. The HEI: beyond BMI? The construction of the HEI measure is discussed in detail in the Online Appendix C. In a nutshell, the HEI has been proposed in 1995 by nutrition experts within the US Department of Agriculture (USDA) (Kennedy et al., 1995; Guenther et al. 2006a, 2006b, 2007; USDA, 2011), and its special attractiveness lies in the fact that it is a global measure of individual quality of diet and nutritional balance, adjusted by the total caloric intake of the subject. This feature makes the HEI particularly fit to complement the Body Mass Index (BMI). The HEI has been recently used as an alternative to BMI in order to disentangle the complex effects of different aspects of healthy weight on socio-economic outcomes: for instance, Bhattacharya et al., (2004) employ the HEI to assess the quality of the diet when exploring the link between food insecurity and poverty, while Bhattacharya et al., (2006) use it in an evaluation of the school breakfast program on the nutrition of children and their families. An
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.
Table 8: Estimated risk preferences: smoking and gender All Active smokers Non smokers Smoke or used to smoke Never smoked Ex smokers Total 0.6917 (0.0415) 0.4804 (0.1634) 0.6536 (0.0298) 0.8675 (0.1499) 0.6977 (0.0423) 0.8184 (0.1405) Men 0.6681 (0.0251) 0.5814 (0.0609) 0.6672 (0.0299) 0.7288 (0.0669) 0.6806 (0.0271) 0.6702 (0.0469) Total 0.6793 (0.0236) 0.5651 (0.0551) 0.6604 (0.021) 0.7965 (0.0861) 0.6891 (0.0251) 0.7279 (0.0644)