Elaboración de sentido y búsqueda de identidad

The univariate probit results from the previous section (6.1) aimed to compare the determinants of smoking and alcohol consumption between two data years (1993 and 2007). This section presents the results for the bivariate probit regression models. Unlike the univariate probit model, the bivariate probit model estimates the smoking and alcohol consumption equations simultaneously. This approach allows the estimation on the likelihood of smoking and alcohol consumption at the same time. The bivariate probit model allows for the error terms of the two equations (smoking and alcohol consumption) to be correlated via some unobservable individual characteristics (correlation coefficient ρ). It also evaluates a bi-dimensional integral over the two distributions of the error terms. Therefore, the Bivariate Probit model is a better methodology than the univariate probit model because it captures precisely the unobservable exogeneity

6.2.1 Testing for unobserved exogeneity

Prior to presenting the regression results from the Bivariate Probit model, I first test whether

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The univariate Probit model’s results for alcohol consumption are generally the same as (or not too dissimilar with) the results from the ordered Probit model. Indicating that the overall conclusion from my univariate Probit model need not to be altered based on merely expanding the original drinking variable (1=drinker, 0=non-drinker) to a more detailed set of drinking variables. For Income and education level,both models are positive and statistically significant, except for the heavy alcohol consumption level (level 5) in the ordered Probit model, in which the results showed a weak significant (at 10 %) for income and education level, see appendix 5 for more detail.

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Bivariate Probit is a better fit than univariate probit by performing a likelihood ratio (LR) test and Wald test. Testing unobserved exogeneity in the Bivariate Probit model consist of seeing whether the correlation coefficient (ρ) between the two error terms is zero or not. The null and alternative hypotheses are H: ρ 0 and H_Á: ρ ´ 0, where ρ represents the correlation coefficient between the residuals from the smoking and alcohol consumption equations. If the null H is not rejected, or I cannot rule out ρ 0, then the results from the bivariate Probit

model is analogous to the univariate Probit model. If that is the case, the error terms for smoking and alcohol consumption are independent. This implies that the unobserved factors affecting the probabilities of being a smoker and alcohol drinker are not significantly correlated.

On the other hand, if H is rejected, or ρ ´ 0, it means that the error terms for the two equations are not independent. This implies that there is evidence of unobservable factors affecting both probabilities of being a smoker and alcohol drinker. Then, the two Probit equations need to be estimated jointly via the bivariate Probit model.

Table 6.3 reports the results of the estimated correlation coefficient (ρ) between the two error terms (smoking and alcohol consumption equations) and 3 test statistics (Z statistic, LR test and Wald test) from the bivariate probit model. As shown in Table 6.3, the estimated correlations coefficient (ρ) are 0.3518 and 0.2592 for 1993 and 2007, respectively. I interpret that the correlation between the error terms in the smoking and the alcohol consumption equations to be positive, suggesting that smoking and alcohol consumption share some unobservable determinants together. Furthermore, the results of the unobserved exogeneity tests (Z statistic, LR test and Wald test) are shown. The LR test and the Wald test as shown suggest that the estimate correlation coefficients in both 1993 and 2007 are both statistically significant from

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zero. The null hypothesis ρ ´ 0, is rejected at the 1 % level for both smoking and alcohol consumption equations. This result suggests that the bivariate Probit model is an appropriate methodology; if two independent probit models are used instead, the results generated will be biased. Therefore, the bivariate Probit model is applied in the following section.

6.2.2 Seemingly Unrelated Bivariate Probit Regressions Results for 2007

Because over 99% of the population enrolled in the NHI program in 2007, the results can be interpreted as the determinants of smoking and alcohol consumption under NHI, rather than looking at NHI’s effect as a whole. Table 6.4 presents the estimated coefficient results, the corresponding standard errors and other important statistics from the smoking and drinking Bivariate Probit models side by side. This allows me to examine the joint determinants of smoking and alcohol consumption for 2007. With respect to demographic variables, Age is statistically significant and negative at 1% level in both the smoking and alcohol consumption models. From Table 6.4, one can see that seniors are less likely to be smokers or alcohol drinkers.

Gender is statistically significant and positive in both models; it implies that males are more likely to be smokers or drinkers than females. Married is statistically significant and negatively related to smoking only, meaning married people are less likely to be smokers.

With respect to socioeconomic variables, comparing to the low income reference group, only people belonged to Income03 (middle- high income group) and Income04 (high income group) are more likely to be drinkers. As to education level, people with higher education are significantly less likely to be smokers; instead, but are more likely to be drinkers.

With respect to health status and health indicators, comparing to the reference group

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level on smoking, suggesting that people who have self-reported a fair health status are more likely to be smokers. As for alcohol consumption, those self-reported either fair or bad and worse are less likely to be alcohol drinkers. People with, or experienced either high blood pressure (HBP) or liver disease are less likely to be smokers; while people who had experienced a stroke are less likely to be drinkers. People with, or have experienced cancer are less likely to be smokers or drinkers. Lastly, people with or have experienced arthritis are more likely to be drinkers.

With respect to medical service utilization, people who have received inpatient or outpatient services in the past year are less likely to be drinkers. This is a possible example of the moral hazard effect on medical service utilization. Recall that copayment rates are set up in order to control medical utilization. Those who have received inpatient or outpatient services in the past year would have had personal experience with paying for various copayments associated with their visits. It is possible that these patients adjusted their unhealthy behavior such as drinking in light of the cost burden from copayments. With respect to the geographic variables, comparing to

Eastern area, people from Northern area and Southern area are less likely to be smokers only; while people from Central area are less likely to be smokers and drinkers.

Timespend (travel time to the hospital) is statistically significant and negatively correlated with smoking and alcohol consumption. This suggests that the more time it takes for people to get to the hospital, the less likely they will smoke and drink. Lastly, there are some contradicting results for the Healthcheck variable: as shown in Table 6.4, people who had regular health checkup in the past year are less likely to smoke, however, but are more likely to drink.

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6.2.3 Seemingly Unrelated Bivariate Probit Regressions Results for 1993

Apart from some exceptions, results from the 1993 model are not too dissimilar to those from 2007. For example, in the smoking model in 1993, the medical service utilization variables ― inpatient and outpatient services― are negative and statistically significant. However, health status is not significant. For alcohol consumption in 1993, education levels are not significant but the regional variables are statistically significant. These aforementioned results are different compared to 2007’s model.

Results of the Bivariate Probit models in 1993 are presented in Table 6.5. With respect to demographic variables, Age is negative and statistically significant at 1% in both smoking and alcohol consumption models; this suggests that seniors are less likely to smoke or drink in 1993.

Gender is positive and statistically significant in both models, implying that males are more likely to smoke or drink. Married is statistically significant and negatively correlated to smoking only, meaning married people are less likely to be smokers in 1993.

With respect to socioeconomic variables, comparing to the low income reference group, only those belonged in Income03 (middle- high income group) are more likely to be drinkers. As to education levels, people with higher educations (diploma and college) are significantly less likely to smoke compared to the less-educated.

With respect to health status and health indicators, comparing to Health01 (good and excellent), Health03 (bad and worse) is negative and statistically significant at 1 % in the alcohol consumption model, implying that people who self-reported their health status as “bad and worse” are less likely to be alcohol drinkers in 1993. HBP is significant and negatively correlated with smoking and alcohol consumption. This suggests that people with, or have experienced high blood pressure (HBP) are less likely to smoke or drink in 1993. People with, or have experienced

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liver disease are less likely to be smokers; on the other hand, people with, or have experienced arthritis are more likely to drink.

With respect to medical service utilization, people who received inpatient services in the previous year are less likely to be smokers or drinkers in 1993; nevertheless, people who received outpatient services in the previous year are less likely to be a smoker only. Lastly, someone who had visited pharmacy in the previous year is more likely to be a smoker in 1993. With respect to other control variables, comparing to Eastern area, people from the Northern,

Southern and Central area are less likely to be smokers or drinkers. Timespend (travel time to the hospital) does not show any significant correlation with smoking or alcohol consumption in the 1993 models. For Healthcheck, people who had regular health checkups during the previous year are less likely to be smokers.