Happiness as a Driver of Risk-Avoiding Behavior

Most governments try to discourage their citizens from taking extreme risks with their health and lives. Yet, for reasons not understood, many people continue to do so. We suggest a new approach to this longstanding question. First, we show that expected-utility theory predicts that 'happier' people will be less attracted to risky behaviors. Second, using BRFSS data on seatbelt use in a sample of 300,000 Americans, we document evidence strongly consistent with that prediction. Our result is demonstrated with various methodological approaches, including Bayesian model-selection and instrumental-variable estimation (based on unhappiness caused by widowhood). Third, using data on road accidents from the Add Health data set, we find strongly corroborative longitudinal evidence. These results suggest that government policy may need to address the underlying happiness of individuals rather than focus on behavioural symptoms.subjective well-being, risky behaviors, effects of well-being, rational carelessness

Happiness as a Driver of Risk-Avoiding Behavior

Understanding the reasons why individuals take risks, particularly unnecessary risks, remains an important question in economics. We provide the first evidence of a powerful connection between happiness and risk-avoidance. Using data on 300,000 Americans, we demonstrate that happier individuals wear seatbelts more frequently. This result is obtained with five different methodological approaches, including Bayesian model-selection and an instrumented analysis based on unhappiness through widowhood. Independent longitudinal data corroborate the finding, showing that happiness is predictive of future motor vehicle accidents. Our results are consistent with a rational-choice explanation: happy people value life and thus act to preserve it.risk preferences, seatbelt usage, vehicle accidents, subjective well-being, happiness

MultiBUGS: A Parallel Implementation of the BUGS Modeling Framework for Faster Bayesian Inference

MultiBUGS is a new version of the general-purpose Bayesian modeling software BUGS that implements a generic algorithm for parallelizing Markov chain Monte Carlo (MCMC) algorithms to speed up posterior inference of Bayesian models. The algorithm parallelizes evaluation of the product-form likelihoods formed when a parameter has many children in the directed acyclic graph (DAG) representation; and parallelizes sampling of conditionally-independent sets of parameters. A heuristic algorithm is used to decide which approach to use for each parameter and to apportion computation across computational cores. This enables MultiBUGS to automatically parallelize the broad range of statistical models that can be fitted using BUGS-language software, making the dramatic speed-ups of modern multi-core computing accessible to applied statisticians, without requiring any experience of parallel programming. We demonstrate the use of MultiBUGS on simulated data designed to mimic a hierarchical e-health linked-data study of methadone prescriptions including 425,112 observations and 20,426 random effects. Posterior inference for the e-health model takes several hours in existing software, but MultiBUGS can perform inference in only 28 minutes using 48 computational cores

Bayesian structural inference with applications in social science

Structural inference for Bayesian networks is useful in situations where the underlying relationship between the variables under study is not well understood. This is often the case in social science settings in which, whilst there are numerous theories about interdependence between factors, there is rarely a consensus view that would form a solid base upon which inference could be performed. However, there are now many social science datasets available with sample sizes large enough to allow a more exploratory structural approach, and this is the approach we investigate in this thesis. In the first part of the thesis, we apply Bayesian model selection to address a key question in empirical economics: why do some people take unnecessary risks with their lives? We investigate this question in the setting of road safety, and demonstrate that less satisfied individuals wear seatbelts less frequently. Bayesian model selection over restricted structures is a useful tool for exploratory analysis, but fuller structural inference is more appealing, especially when there is a considerable quantity of data available, but scant prior information. However, robust structural inference remains an open problem. Surprisingly, it is especially challenging for large n problems, which are sometimes encountered in social science. In the second part of this thesis we develop a new approach that addresses this problem|a Gibbs sampler for structural inference, which we show gives robust results in many settings in which existing methods do not. In the final part of the thesis we use the sampler to investigate depression in adolescents in the US, using data from the Add Health survey. The result stresses the importance of adolescents not getting medical help even when they feel they should, an aspect that has been discussed previously, but not emphasised