Descriptive Statistics
The dataset used consists of 267 individuals with an average age of 56.4 and predominantly males (55.4%). We found that 60% of the individuals had a low education level. Table 1 provides additional statistics on the dataset. From these average values in Table 1, we found that the obesity rate grows slightly with age (12% on average for individuals aged 50-55, 13% for individuals aged 55-60 and 19% for individuals aged 60-65). Moreover, alcohol consumption declines slightly with age, while the smoking rate only declines after the age of 60. In addition, the employment rate decreases with age (from 71% for those aged 50-55 to 36% for those aged 60-65). It is also notable that only a relatively small proportion of older people perform physical activity (only 21% of those aged 50-65 perform physical activity). Furthermore, for each age group the SSP declines with the target age. However, for the same target age, the average SSP increases with age groups. At the target age of 75, for example, the average SSP is 60% for individuals aged 50-55, 66% for those aged 55-60, and 71% for those aged 60-65.
The self-assessed life expectancy function
We plotted the kernel densities per gender and age group and we added the national life expectancies trend line (red dashes) for comparison purposes and for each graph in order to better describe the estimated life expectancy. Figure 1 shows that the distribution of estimated life expectancy is not the same among the genders and age groups. Individuals in Dabou estimate their survival to be higher than the national average, and this situation is pronounced for males. However, for both males and females, and for the three age groups considered herein, the distribution of estimated life expectancies appears to be a combination of at least two probability distributions. In each group, there is a proportion of individuals who had a low estimation of their life expectancy, and those with medium or high estimations of their life expectancy. The same holds for the estimated global life expectancy distribution (Figure 2 in Appendix A). Several reasons, like changes in self-reported health [2] or perceived health conditions, might explain these differences.
The red dashes for each sex and age group are the national life expectancy calculated using the actuarial method by the National Statistics Office in Côte d’Ivoire. The blue curve represents the kernel density of the estimated subjective life expectancy. For males aged 50-55, the national life expectancy is 73.5 (red dashes, by actuarial method), while the kernel density shows that the major part of the male aged 50-55 has an estimated subjective life expectancy higher than 73.5 (the density distribution is primarily located at the right side of the red dashes).
Determinants of the self-assessed life expectancy
The determinants of the estimated life expectancy were modeled through a finite mixture of regression models. Based on the Akaike Information Criterion (AIC) and the log-likelihood, the model with four clusters/components was selected. Table 1 in Appendix A presents the AIC and log-likelihood values for the models with two, three and four clusters/components.
The results of this selected model are presented in Table 2 and illustrate the effects of concomitant variables on the probability of being in each cluster and Table 3 for the determinants of the estimated life expectancy. Some descriptive statistics about the clusters are given in Table 2 in Appendix A. The average probabilities of being appropriately classified into each cluster are all above 80%, indicating that the model has good classification power. The first cluster represents 24.73% of the population, and the average subjective life expectancy in this cluster is 79.51 years. The second cluster contains 20.97% of the sample. The average subjective life expectancy of this cluster is quite similar to that of cluster 1 (78.89 years). That being said, compared to individuals in cluster 1, which reported a poor state of health reduces individual probability while having a mother alive or thinking that health limits the working capability increases the probability to be in cluster 2. Individuals in cluster 3 (33.33% of the sample) are characterized by deteriorating health and a chronic condition and the average subjective life expectancy is the highest (80.02 years). Cluster 4 represents 20.97% of the population and is the cluster with the lowest average subjective life expectancy (77.79 years). Having a chronic condition reduces an individual’s likelihood to be in cluster 4 while having a mother alive has the opposite effect. These results are consistent with the literature because individuals with a poor health state are pessimistic about their life expectancy [7]. Additionally, the literature confirms that having a parent alive increases individuals’ subjective life expectancy [10]. However, we only found evidence that having a mother alive affects subjective life expectancy. There is no evidence of the effect of having a father alive or both parents alive on the subjective life expectancy. Turning to the determinants of subjective life expectancy, there are some differences across clusters. In terms of demographic characteristics, couples have increased subjective life expectancy for clusters 1 and 2, while having an opposite effect on cluster 4. Nonetheless, there is no significant effect of being a couple on the subjective life expectancy of individuals in cluster 3. In both the highest (cluster 3 – optimistic individuals) and the lowest (cluster 4 – pessimistic individuals) subjective life expectancy clusters, being male increases the subjective life expectancy. However, this positive effect is reduced when the age increases. The opposite effect is observed for individuals in the two middle clusters (cluster 1 and 2 – moderate individuals) in which being a male decreases the subjective life expectancy and this effect slows as age increases. Apart from cluster 1, where aging is negatively correlated with subjective life expectancy, estimates reveal a positive and significant effect of aging on subjective life expectancy. This finding is consistent with the results reported in the literature on the same topic [7]. Living in rural areas negatively affects the subjective life expectancy for individuals for the two middle clusters (clusters 1 and 2). However, for both individuals with the lowest and highest estimates of subjective life expectancy (clusters 3 and 4), living in rural areas increases the predicted life expectancy.
The effects of obesity also depend on the clusters. We found no evidence for an effect of obesity on subjective life expectancy for individuals in the lowest cluster. However, for individuals in the highest two clusters of subjective life expectancy, being obese reduces the subjective life expectancy, while for those in cluster 2, obesity seems to increase their subjective life expectancy. In terms of unhealthy behavior, we found that smoking reduces subjective life expectancy for individuals in the highest cluster, but also for those in cluster 2. However, for those in the lowest cluster and those in cluster 1, smoking has a positive effect on the subjective life expectancy. This result has been explained in the literature as the optimism of smokers regarding their own survival [12]. Excessive alcohol consumption also reduces subjective life expectancy in all clusters, apart from individuals in the lowest cluster for whom it increases subjective life expectancy. Rappange et al. also report that excessive alcohol consumption is positively associated with SSPs for some categories of individuals [13]. For individuals with the highest (cluster 3) and lowest (cluster 4) subjective life expectancy, we found that performing physical activities increases individual subjective life expectancy, while for those in the middle clusters (clusters 1 and 2), there is a decrease in subjective life expectancy. Not having a specific diet increases subjective life expectancy for individuals in all clusters, apart from those in the first middle cluster (cluster 1), for whom it reduces the subjective life expectancy.
In terms of employment, we found that being employed reduces an individual’s subjective life expectancy in both the highest and lowest subjective life expectancy clusters, while the opposite effect was found for the two middle clusters. Having a low education/school level reduces an individual’s subjective life expectancy in all clusters, apart from those in the first middle cluster, for which no significant effect was observed. This finding is consistent with the literature that highlights the heterogeneous effect of education on subjective life expectancy [7,13]. Estimates also show that the effect of income on an individual’s subjective life expectancy is not linear. The effects of variable income on subjective life expectancy are not linear across clusters. A U-shape effect is observed for individuals in the two highest clusters, while the opposite effect was found for those in the two lowest clusters. In other words, for those in the two highest clusters of subjective life expectancy, an increase in household per capita income increased the subjective life expectancy, but this increase abates when the household income per capita increases. The reverse effect is observed for those in the two lowest clusters. This result implies that for the pessimistic clusters, an increase in per capita income does not enhance the perceived life expectancy.
The effects of living standards on subjective life expectancy are also very heterogeneous. Consuming underground water reduces subjective life expectancy for those in the two highest subjective life expectancy clusters and increases subjective life expectancy for individuals in the two lowest clusters. Having toilets that flush increases the subjective life expectancy for individuals in all clusters, apart from those in the highest cluster where the opposite effect is observed. Additionally, we found that having a septic tank increases subjective life expectancy for the two lowest clusters, but reduces the subjective life expectancy for those in cluster 1. For an individual in the highest cluster, no significant effect was found.