In our modelling, we include income and other covariates that have been identified in the literature as important factors. We report results for the full sample and for males, females, homeowners and non-homeowners separately.
Our findings show that exposure to a natural disaster in Australia has a negative and statistically significant effect on reported life satisfaction. This result is consistent with the findings from Carroll et al. (2009) on the impact of Australian droughts and Luechinger and Raschky (2009) on the impact of floods in Europe. On average, a person who had experienced home damage from natural disasters in the past 12 months reported a 0.094 point lower life satisfaction score on the 10-point scale compared to the reference group. This is a net adverse effect of natural disasters which already captures the counter effects of some ‘resilience’ factors such as higher income, increased financial security or better health outcomes.
Experiencing home damage from a natural disaster would need to be compensated by changes in other personal circumstances for an individual to remain at the pre-disaster level of life satisfaction. From the entire sample, we identify other determinants to life satisfaction. As expected, life satisfaction is positively related with household income growth, improved long-term health and being employed.
Owning a home and being married also increases life satisfaction. Interestingly, we do not find a positive link between higher education levels and life satisfaction.
4.1 Gender difference
Natural disasters impact the life satisfaction of women and men differently. To show that, we split the full sample into women and men and estimate the results separately for the two groups. We find that natural disaster experience had a larger impact on women than men. Among the individuals who went through natural disasters, women experienced a 0.113 point decrease in life satisfaction while men experienced a 0.064 point decline. However, the effect on men is not statistically significant.
This result is consistent with findings in the wider economic literature. Emerson (2012), Hazelegar (2013), Prada (2015), Thurton et al. (2021) all demonstrate that women and girls bear a disproportionate burden of disaster-related impacts. The gender difference in wellbeing impact that we observe likely reflects the higher level of disaster vulnerability of Australian women compared to Australian men. This is thought to be directly related to their relatively poor rate and level of access or control of key economic resources (ABS Gender Indicators, Workplace Gender Equality Agency reports). These key resources in disaster, namely, a secure income, access to savings or credit, employment with social protection, marketable job skills, education and training, and control over productive resources, all help shape the way men and women prepare for, react to, are impacted by and recover from disasters.
On the other hand, Australian policy on disaster recovery support has been gender blind. Hazelegar (2013) shows that economic recovery often targets relief funds to male-dominated employment areas such as construction and landscaping. For a specific example, we cite the work of Alston (2009) which argues that the federal and state governments’ emergency support measures for drought discriminated against women in rural Australia as the policies treated family farming as a unitary male pursuit. In such instances, it is evident that men have indirectly benefitted more from disaster recovery plans.
Table 3: Impact of disaster experience on life satisfaction score
| VARIABLE | FULL SAMPLE | FEMALE ONLY | MALE ONLY | HOMEOWNERS ONLY | NON-HOMEOWNERS ONLY | |
|---|---|---|---|---|---|---|
| Disaster experience | -0.094** | -0.113** | -0.064 | -0.091** | -0.210* | |
| (0.041) | (0.057) | (0.059) | (0.043) | (0.116) | ||
| Log household income | 0.077*** | 0.090*** | 0.065*** | 0.069*** | 0.080*** | |
| (0.012) | (0.018) | (0.017) | (0.013) | (0.031) | ||
| Household size | 0.022 | -0.096** | 0.128*** | -0.014 | 0.109 | |
| (0.030) | (0.044) | (0.040) | (0.035) | (0.071) | ||
| Age | -0.007 | -0.085** | 0.075* | -0.009 | 0.009 | |
| (0.028) | (0.041) | (0.039) | (0.031) | (0.069) | ||
| Age square | 0.000*** | 0.000** | 0.000*** | 0.000*** | 0.000 | |
| (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | ||
| Tertiary education | -0.031 | 0.036 | -0.140 | -0.041 | 0.033 | |
| (0.072) | (0.097) | (0.108) | (0.094) | (0.135) | ||
| Long-term health problem | -0.153*** | -0.167*** | -0.135*** | -0.111*** | -0.286*** | |
| (0.017) | (0.024) | (0.024) | (0.018) | (0.042) | ||
| Employed | 0.215*** | 0.095*** | 0.459*** | 0.148*** | 0.350*** | |
| (0.022) | (0.028) | (0.038) | (0.026) | (0.046) | ||
| No. of children | -0.107*** | -0.079** | -0.132*** | -0.100*** | -0.098** | |
| (0.020) | (0.032) | (0.026) | (0.024) | (0.048) | ||
| Married | 0.134*** | 0.124*** | 0.142*** | 0.142*** | 0.091 | |
| (0.027) | (0.038) | (0.038) | (0.032) | (0.068) | ||
| Homeowner | 0.104*** | 0.107*** | 0.095*** | |||
| (0.022) | (0.032) | (0.031) | ||||
| Living in urban area | -0.003 | -0.035 | 0.020 | 0.060 | -0.139 | |
| (0.050) | (0.069) | (0.073) | (0.079) | (0.098) | ||
| Smoking | 0.001 | -0.016 | 0.015 | 0.010 | -0.023 | |
| (0.013) | (0.020) | (0.017) | (0.015) | (0.028) | ||
| Drinking alcohol | 0.001 | 0.006 | -0.008 | 0.007 | -0.010 | |
| (0.004) | (0.005) | (0.006) | (0.004) | (0.009) | ||
| Constant | 5.503*** | 8.887*** | 1.639 | 5.687*** | 4.442 | |
| (1.391) | (1.969) | (1.954) | (1.564) | (3.098) | ||
| Individual fixed effects (FEs) | Yes | Yes | Yes | Yes | Yes | |
| State FEs | Yes | Yes | Yes | Yes | Yes | |
| Observations | 35 747 | 18 905 | 16 842 | 27 730 | 8 017 | |
| R-squared | 0.014 | 0.013 | 0.024 | 0.011 | 0.027 | |
| Number of id1 | 4 484 | 2 434 | 2 050 | 3 843 | 1 607 | |
Notes:
*** is significant at 1% level, ** at 5% level, * at 10% level.
4.2 Home ownership
Recent studies in subjective wellbeing have identified a strong positive relationship between life satisfaction and home ownership – with homeowners at all levels of income exhibiting higher levels of satisfaction compared to those who rent or do not own their homes. This can be seen in, for example, Zumbro (2014) for Germany, Zhang (2018) and Ren, et al. (2018) for China, and Dockery and Bawa (2019) for Australia. Valenzuela, et al. (2014) further identifies that home ownership is an important element in reducing the gap in relative welfare levels between households in Australia.
Therefore, it is worth understanding if and how homeowners and non-homeowners are affected differently by having their homes damaged in natural disasters. To measure this, we partitioned the population into homeowners and non-homeowners and applied our model separately to each group to uncover any systematic differences in the results. Results are presented in the last two columns of Table 3. For homeowners, our analysis shows that experiencing house damage from a natural disaster will reduce life satisfaction by 0.091 points. The negative impact is much larger for non-homeowners, with a 0.210-point reduction in life satisfaction. At face value, this implies that the impacts of natural disaster accrue more to non-homeowners than they do to homeowners, but why this is so may be more complex than can be immediately surmised.
First, the social and economic profiles for homeowners and non-homeowners are very different. Homeowners tend to be older, have higher incomes and have homes that are larger and/or better quality. In contrast, non-homeowners tend to be younger, have less work experience and have lower income levels on average (see Table 1 for cohort information). Results from Dockery and Bawa (2019) suggest that homeowners tend to have higher levels of life satisfaction as a result of their higher financial and social security, in part due to the benefits of residential stability and greater community engagement. Furthermore, the study finds that homeowners have better physical and mental health outcomes than non-homeowners. The precise explanation of why we observe greater negative effects accruing to non-homeowners will need to be investigated more systematically to reach a definitive conclusion. From what we can infer here, our findings suggest that people with greater assets/tenure security have higher economic resilience.
4.3 Quantifying costs of natural disasters
In this section, we translate our results to dollar values for any losses incurred or gains realised as indicated by our modelling exercise above. In particular, we use the estimated coefficients for disaster experience (δ) and income (β) to approximate an individual’s compensating variation, or their willingness to pay (WTP) to avoid a natural disaster (∆D). The resulting figure will be tantamount to the full cost compensation due to the individual post-disaster, if we were to restore their life satisfaction and wellbeing to pre-disaster levels.
To achieve this, we consider two situations:
(i) a reduction of the probability of a damage from 1.5 per cent to 0 per cent, that is, ∆D=0.015, by assuming that the likelihood of a potential risk for a disaster damage is 1.5 per cent2
(ii) a complete avoidance of damage from a natural disaster, which implies ∆D = 1.
To compute, we apply equation (3) and use the sample’s average annual household income over 11 years, which is $133,105. The calculated compensations are presented in Table 4. The associated estimated compensation for the prevention of certain house damage from a natural disaster for Australians is $162,492 (in 2019 Australian dollars). This is an estimate of the maximum amount the average Australian is willing to pay to avoid a disaster event, given that the income level and other parameters are unchanged. For a reduction of the probability of a natural disaster by 1.5 per cent, it is estimated that the average individual would be willing to pay $2,437 or 1.8 per cent of annual household income.
Table 4: Quantified costs of natural disasters (in 2019 Australian dollars)
| Amount of income an individual is willing to pay for | Absolute | In per cent |
|---|---|---|
| (a) Decrease in probability of home damage from a natural disaster by 1.5 per cent | $2,437 | 1.8% |
| (b) Prevention of damage from a natural disaster | $162,492 | 122% |
Note: Values were computed based on an annual household income of $133,105 which is the sample’s annual average income over 11 years.
We compare our results to other studies using similar approaches. Luechinger and Raschky (2009) estimates that the average WTP for the prevention of floods in the affected regions of residence in Europe and the United States is $6,505 (in 2004 U.S. dollars) or 23.7 per cent of an average household income. For a 2.6 per cent decrease in the likelihood of annual flood, an individual would be willing to pay $195 (about 0.7 per cent of their annual household income). In estimating the cost of the 2002 drought, Carroll et al. (2009) finds each quarterly drought is equivalent to the loss of $18,000 (in 2001 Australian dollars) in household income.
Our total cost estimate for natural disasters is considerably higher than the above two studies. There are a number of potential explanations for this discrepancy. Firstly, in our study, the type of natural disaster is not specified, suggesting natural disasters that are not evaluated in the above two studies, such as bushfires, may require different monetary compensation. Secondly, we define natural disaster experience as when a respondent has had their house damaged by a disaster. In contrast, the two earlier studies represent disaster experience as whether the individual resided in the region in which the disaster occurred. Hence, the intangible cost estimates will be greater in our analysis as we solely focus on individuals confirmed to have suffered damages from a disaster. Thirdly, the data frequency of our study differs from the two earlier studies, resulting in different present value of an averted natural disaster in life satisfaction terms. Data on disaster experience and life satisfaction used in our study were collected on a yearly basis. However, estimates in Luechinger and Raschky (2009) were based on the survey question over an 18-month period while Carroll et al. (2009) used data collected on a quarterly basis. Further research based on better quality data is warranted to obtain more reliable estimates of costs for natural disasters.
Footnotes
[1] This represents people who responded in all 11 waves in our balanced dataset.
[2] The probability of natural disasters during the period 2009 to 2019 ranging from 0.7 per cent to 1.5 per cent.
Updated