Health, Wellness and Tourism: healthy tourists, healthy business ...

As some of the settlement characteristics were not significant in explaining the dependent
variable, over a number of steps we dropped them from the model (one by one), hence
increasing the explanatory power of the model.
After omitting variables that did not fit in the model , the final equation became:
Happiness=b0+b1*agegroup+b2*familystat+b3*educat+b4*econact+b5*market+b6*GP+b7
*nursery+b8*library.
3.4. Measuring the impact of tourism on subjective well-being
In order to set up the equation, we need to find those settlements first that play an important
role in tourism. According to the classical approach, a settlement’s role in tourism is assessed
by the number of „guest nights” (Michalkó 2007). Therefore, the total number of guest nights
is the key indicator of tourism in our study. Settlements registering even a single guest night
are therefore considered "tourism-affected" settlements.
In addition, our analysis distinguished "wellness tourism-affected" settlements. This group
included all settlements that possessed some sort of medical or thermal spa or cave bath.
Results
Settlement-level approach: creation of a “happiness index” on the settlement level
In addition to assessing the factors that influence individual happiness, we would like to see
what kind of hierarchy can be established among settlements, when we look at the happiness
of the people who live there. In the model presented previously we were using variables
affecting subjective happiness that would apply on a settlement level also.
Why is this level of abstraction needed? It is easy to see that no ranking can be established
among settlements if we only consider individual happiness of their inhabitants; not unless we
also use a database that includes enough related data for each and every settlement. Such
database does not exist unfortunately, therefore we must tackle the problem differently.
Variables of the regression model on the database of answers on individual happiness are
“translated” into a settlement level, and are then weighted with the odds ratios from that
model. We can establish a ranking among the settlements then by simply averaging values
from the weighting.
Based on the HCSO TSTAR database, as settlement-level equivalents of variables in the
regression model, we used the indicators summarised in Table 3.
Table 3. Equivalents of determinants of subjective well-being on the settlement level
Number. Socio-demographic characteristics Settlement level indicators
1.
influencing the individual’s subjective
well-being
Age Proportion of the age group within the
population of the town
2. Marital status Ratio of the population of given family status
within the population of the town
3. Highest level of education Proportion of population of a given education
level within the population of the town
4. Economic activity Ratio of population of given economic
activity within the population of the town