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Household disaster resilience is a dichotomous dependent variable measured as described in section 6.1 (high/low resilience). There were 14 independent variables measuring human capital that were either cate- gorical, interval, or continuous. Of the 14 independent variables tested in the model, 12 were significantly related to disaster resilience (see Table 6.14). However, two variables were excluded from the final model, because age was not significantly related to the dependent variable (disaster resilience) and the highest level of education, although significant, was multicollinear to other variables of human capital (VIF, 8.3).

Logistic regression methods explore the relationships between two or more independent variables and one dependent variable. The probable outcomes of a single trial are modeled as a function of the independent variable using a logistic function (Peng & So, 2002). Table 6.15 presents the regression results of the rela- tionship between human capital and disaster resilience at the household level in the study area. All the variables in the model are shown in column two of Table 6.15. Column three provides the coefficients (B) and statistical significance (p-value) of the variables. Column four presents the coefficients as odds ratios (OR), and column five shows the odds ratio (OR) in the 95% confidence level (95% CI). Odds ratios indicate that a one unit change in the explanatory variable is associated with an “X times” likelihood of high disaster resilience. These coefficients could also be explained as percentage changes in the likelihood of the depend- ent variable.

Regarding formal education, two variables (fluency in English and number of years of formal education) were positive and significant (p < .001 and p < .050, respectively). Individuals with relatively more formal education and English language fluency were more likely to show high resilience. For each additional year of formal education, households were 1.18 times as likely to have a high level of resilience (OR: 1.188, 95% CI: .941–1.499) compared to individuals with a low level of formal education. Individuals with language fluency in English were 1.40 times as likely to have a high level of resilience (OR: 1.188, 95% CI: .941– 1.499) compared to individuals with no language fluency (OR: 1.406, 95% CI: 1.242–1.591).

Table 6.15: Logistic regression results of the relationship of human capital and disaster resilience

Pillars of human capital Variables of human

capital B Exp(B)/OR

95% CI for EXP(B)/OR

Lower Upper

Knowledge from for- mal school, college, and university educa- tion

Years of formal educa-

tion .172* 1.188 .941 1.499

Fluency in English

speaking .340*** 1.406 1.242 1.591 Knowledge from vo-

cation and technical training (VET)

Vocational education and training

.360* 1.434 1.045 1.966

Knowledge from hands-on learning

Practical skills .268** 1.308 1.119 1.528 Knowledge from par-

ticipating and interacting

Knowledge achieved from the participation of health care program

.458** 1.580 1.190 2.097

Knowledge received from adult education pro- gram

1.285*** 3.614 2.375 5.500

Knowledge gained from voluntary interactions

.947*** 2.578 2.161 3.076 Obtained knowledge

from the participation economic co-operatives program

.662*** 1.939 1.660 2.265

Knowledge obtained from awareness program

.251** 1.286 1.069 1.547 Knowledge from ex-

perience

Experiences from the previous disasters

.342*** 1.310 1.091 1.731 Experiences from deal-

ing with cyclone

-.806*** .446 .334 .596 Ability to work Working ability without

any physical difficulties

.095** 1.100 .952 1.270

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As shown in Table 6.15, VET and practical skills significantly and strongly (p = .025 and p = .001, respec- tively) contributed to enhancing disaster resilience. With regard to formal education, individuals who were fluent in English language were 1.406 times as likely as those who were not fluent to have high level of disaster resilience (OR: 1.434, 95% CI: 1045–1.966). Similarly, individuals who gained practical skills were 1.308 times as likely to have a high level of resilience (OR: 1.308, 95% CI: 1.119–1.528). Notably, knowledge obtained from participation and interaction in learning programs had ORs significantly higher than that of formal education or VET. Diverse knowledge-gaining programs, such as healthcare (p < .01), adult education (p < .001), volunteer interactions (p < .001), and economic co-operatives (p < .001), were significantly and positively associated with disaster resilience. Each additional family member who gained knowledge from participating and interacting in programs was associated with a higher probability of high disaster resilience. The probabilities of high disaster resilience were as follows: healthcare programs (1.580 times), adult education (3.614 times), volunteer interactions (2.578 times), economic co-operatives (1.939 times), and awareness programs (1.286 times) compared to low disaster resilience. Interestingly, adult edu- cation had the strongest effect on disaster resilience. This was probably because of the various livelihood skills and knowledge individuals derived from adult education. The coastal individuals gained various types of knowledge through adult education such as creating household savings, using pond water during disas- ters, using livestock manure (i.e., cow dung) in agriculture land as fertilizer, and so on, which enhanced disaster resilience.

Experience gained from previous disasters over the past 10 to 15 years was significantly and positively associated with disaster resilience. However, experience gained from various activities dealing with disas- ters was significantly and negatively associated (OR: -.860, 95% CI: 1.069–1.547) with disaster resilience. The regression results revealed that individuals who had experienced previous disasters were 1.310 times as likely to have low disaster resilience. In addition, the ability to work was significantly (p < .01) and moderately (OR: 1.100, 95% CI .952–1.270) associated with disaster resilience, which meant that a greater ability to work increased the probability of high disaster resilience.

In addition, a classification table that demonstrated the validity of the predicted probabilities was created. The first two rows in Table 6.16 present the two possible outcomes, and the two columns under the “pre- dicted” caption are the probabilities of low and high disaster resilience in terms of human capital. Table 6.16 shows that the overall correct prediction was 72.9%, which is not a very high rate but still an improve- ment over the chance level.

Table 6.16: Observed and predicted frequencies at the 0.50a _cut-off

Observed

Predicted

Level of disaster resilience % Correct

Low High

Level of disaster resilience Low 3104 222 93.3

High 1074 390 26.6

Overall % correct 72.9

Pseudo R-Squared = .162; Hosmer and Lemeshow test: Chi-square = 7.822(8)

To assess the adequacy of the regression model, the investigation considered goodness-of-fit statistics. Goodness-of-fit assesses the fit of a logistic model against actual outcomes (Peng et al., 2002). The Hosmer- Lemeshow test was used to assess the goodness-of-fit. The results were a χ² of 7.822(8), which was not statistically significant (p > .05), indicating that the model fit the data well (Table 6.16). A non-significant Hosmer-Lemeshow test suggests adequate fit in a logistic model (Hosmer et al., 1997).

Regarding the research question on “the relationship between human capital and household resilience to cyclones and storm surges,” the logistic regression results revealed that human capital is significantly asso- ciated with disaster resilience.

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CHAPTER SEVEN: DISCUSSION AND CONCLUSIONS

This chapter discusses the results of previous chapters in the context of existing literature, beginning with the status of human capital and the other four forms of capital: social, financial, physical, and natural. It then discusses the relationships among all five forms of capital and the contribution of human capital to the enhancement of disaster resilience in the study area. Finally, the chapter concludes with the limitations of the study and recommendations for further research.

7.1 Household capitals in the study villages