**3. Results**

### **3.1 Descriptive statistics**

The sample comprises 5,701 respondents older than 65, of whom 85.4% are retired. Only 332 respondents say that they have private voluntary health insurance, the large majority (about 94%) do not. Most of them are not entitled to any other insurance coverage apart from the NHS, and only about 10% are covered by ADSE, the occupation-based insurance plan for public workers (**Table 2**).

The remaining descriptive statistics for the independent variables are also shown in **Table 2**. The majority of the people in the sample are women (*circa* 61%) and most of them are aged between 65 and 75. Portuguese older people have very low levels of education, with more than 80% having less than 6 years of schooling. They have very low levels of income, about 50% receive an income valued in the first and second quintile of per capita household income. A large share of the people in the sample live in rural areas (*circa* 43%), and most of them are married or used to be married.

Finally, regarding their health status, the majority of older Portuguese assess their health status below the median level and about 86% of them report suffering from at least one chronic disease.

Other descriptive statistics regarding the distribution of respondents with private health insurance across income, education, and self-assessed health are shown in **Table 3**. Considering those individuals who said they had private health insurance (332 people), their distribution across income shows that a larger share of respondents has a high-income level. The distribution of people with health insurance across levels of education has two peaks, one at 6 years of schooling and the other at 17 years of schooling. The distribution of the health status of people having an insurance policy shows that most people with health insurance report a health status better than fair.

To finish the description of the variables, we now report the correlation between health risk indicators. The pairwise correlation between SAH and suffering from chronic diseases is equal to −0.364 and between SAH and smoking it is equal to 0.107, both for a statistical significance of less than 0.001. The tetrachoric correlation between smoking and suffering from a chronic disease is equal to −0.257 for an identical statistically significant level. So, there is no strong correlation that could prevent the joint utilization of these variables in a regression analysis.

### **3.2 Model results**

The results obtained with the estimation of the probit for having voluntary health insurance are presented in **Table 4**. The statistically significant coefficients at 5% are marked with \*.

The estimated coefficients show that as someone gets old or is single, the probability of having private health insurance decreases, while for higher income or education levels that probability increases.


*Voluntary Private Health Insurance Demand by Older People in a National Health Service… DOI: http://dx.doi.org/10.5772/intechopen.105100*


#### **Table 2.**

*Descriptive statistics.*


#### **Table 3.**

*Distribution of respondents with voluntary health insurance.*

Regarding the insurance status of people, being a beneficiary of ADSE or another occupation-based insurance decreases the odds of having private VHI. Lastly, the results for health status and health-related behavior are mixed. On the one hand, higher levels of SAH may be related to having VHI, but on the other hand, suffering from a chronic disease is also positively related to having VHI;

*Voluntary Private Health Insurance Demand by Older People in a National Health Service… DOI: http://dx.doi.org/10.5772/intechopen.105100*

additionally, the observable behavior of smoking results in a lower likelihood of benefiting from VHI coverage.

The marginal effects associated with the most important and statistically significant coefficients are presented in **Table 5**.

These effects represent the change in the probability of having a VHI policy after the discrete change from the base level of the independent variable. In this way, the change to the oldest age groups implies a decrease of about 4% in the probability of having VHI, while the change from the lowest income quintile to the highest expresses an increase of 11% in the probability of being covered by VHI. Being a member of ADSE results in a 6% less chance of having VHI, and finally, the change from poor health status to a better one increases the likelihood of benefiting from a VHI; for instance, it increases almost 4% for people reporting very good health.
