Abstract
In the present research, we investigate whether cultural value orientations (CVOs) and aggregate personality traits (Big-5) predict actual levels of alcohol consumption, smoking, and obesity across 50 countries using averages derived from millions of data points. Aggregate traits explained variance above and beyond CVOs in obesity (particularly neuroticism and extraversion), while CVOs explained variance beyond aggregate traits in alcohol consumption (particularly harmony and hierarchy). Smoking was not linked to aggregated traits or CVOs. We conclude that an understanding of the cultural correlates of risky health behaviors may help inform important policies and interventions for meeting international sustainable development goals.
Highlights
Keywords public data, smoking, obesity, alcohol, health, personality traits, cultural value orientations. Tackling issues such as obesity, abusive alcohol consumption, and smoking are fundamental to meeting the United Nations Sustainable Development Goals for 2030: the goal to ensure healthy lives and promote wellbeing for all at all ages (United Nations General, 2015). This is unsurprising given that alcohol consumption, smoking, and obesity underpin a number of noncommunicable diseases (NCDs; e.g., type 2 diabetes and liver disease) and are responsible for a significant amount of deaths (World Healthy Organization [World Health Organization (WHO)], 2013)
Few studies have investigated the psychological predictors of risky health behaviors at an aggregated country level
(2017) explained that harmony may be positively correlated to alcohol consumption at the country level because cultures high in harmony regulate how their members relate to the social world via an emphasis on appreciation and “fitting in” (Schwartz, 2006)
Summary
The data for the present study were obtained from a number of different online sources. We only included those 50 countries in the analysis for which we had data available for traits, CVOs, health variables, and control variables. Estimates of national obesity prevalence (percentage of men and women aged 20+ with a BMI ≥ 30 kg/m2) were obtained online from the NCD Risk Factor Collaboration (NCD-RisC) website (www.ncdrisc.org) Full information about this data source is presented in NCD-RisC (2017). Control variables: Human Development Index values for 2015 (HDI; which captures the dimensions of health [life expectancy at birth], education [years of schooling] and standard of living [GNI per capita]) were obtained from the United Nations Developmental Programme website Ridge Regression: We knew that the number of independent variables in our multivariate linear models would be large considering the relatively small sample size In such instances, standard regression estimators can yield unstable coefficient estimates and inflated SEs (Bühlmann & Van De Geer, 2011). Using a statistical procedure that was developed for much less reliable data such as null hypothesis significance testing is a very conservative approach and may result in many type-II errors
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
