Month: July 2014

Smoking decisions and information-Emprical Evidences

Quotes from “Do health changes affect smoking”

Our initial intuition is that individuals may not know with certainty the health consequences
of smoking: their beliefs about smoking’s dangers will determine their consumption.
These beliefs will be updated using information from both one’s own health
developments when smoking, and from those amongst other smokers.2 However, a negative
relationship between own past health developments and current smoking is also consistent
with a Grossman model of health demand (in which all parameters are known with certainty)
when there are health shocks.We cannot distinguish empirically between these two
models.

It seems this paper recognize the difficulties to empirically identify the Grossman model and the learning model.

Quote from “Do smokers respond to health shocks”

The conventional wisdom of risk communication (Fischhoff, 1989, Slovic et al. 1985) holds that indirect
experience from public information programs or news media causes people to believe the events at risk can happen,
but the net effects on behavior are smaller than what might be expected. One explanation is that people believe their
personal experience would be better than the “average”conditions reported in these programs.

And they have a very serious way of classifying smoking related health shocks and general health shocks

 To ensure that health shocks are serious health events and hence powerful “shocks,” we include only reports of heart
attack, congestive heart failure, and stroke requiring that the person report at least three days in the hospital between
waves 1 and 2.

For general health shocks, we use the onset of serious medical conditions that are specifically collected by HRS
and that are not linked conclusively to smoking. General medical shocks include the onset of diabetes that resulted in
a hospitalization.

Findings

Current smokers react to only smoking-related shocks, and the other groups modify their longevity expectations in response to both types of health shocks.

Other work

As with the chi-square analysis, our primary hypothesis is that the smoking-related (SSt21) and general health (GSt21) events provide new information inducing a revision of a respondent’s subjective longevity beliefs.Our unobservable indicator of the risk equivalent of new information, rt, is hypothesized to be a function of these
measures as in equation 2.

There are important differences between this model and Viscusi’s analysis. In his case, the measure of prior subjective
beliefs, Pt21, was not observed because he had a single cross section. Equally important, because his analysis relied
on a single cross section, the only source for the updating effect hypothesized to underlie equation (3) was the difference in the information available to different demographic groups. That is, one might hypothesize that young adults with higher levels of education have greater information about smoking than do those adults who did not complete high school. Comparing the two groups, the differences in their education would be hypothesized to reflect different amounts of information. Unfortunately, there is no basis for discriminating between this explanation and one that suggests that other variables correlated with education are different.

 

 

Living Rationally Under the Volcano? An Empirical Analysis of Heavy Drinking and Smoking

In table 2 ,it would be interesting to see for each transition, how does health status evolve.

key part of the model :

First, and most importantly, an individual does not know how his health
status will evolve over time. In particular, the relationship between smoking and drinking
habits and mortality and morbidity status is stochastic. An individual who engages in heavy
drinking and smoking does not necessarily experience bad health outcomes, but rather has
a higher probability of experiencing negative health shocks in the future. As individuals
experience negative health shocks they will update their believes about the remaining life
expectancy and may change the behavior

But from my reading of the paper, the transition of the health process is not explicitly discussed. And it seems the parameters of the stochastic process are known. My goal is to change the parameters to be unknown and use bayesian updating. The key question is how does adding such element change the model predictions and out-of-sample performance.