Let us start the analysis by considering scenario Non-Adapt in which a non-adapted Swiss economy is hit by a 2003-like heat wave. We split the analysis of the impacts in two parts: first we present the direct impacts, i.e. the impact on mortality. Second, we describe the general equilibrium impacts that result from the heat wave induced shock in mortality. Throughout chapter 3.4.2, we use the benchmark (no heat wave and no adaptation) as reference.
3.4.2.1 Impact on mortality
The impact of a 2003-like heat wave on mortality of cohorts of age 60 and older in a non-adapted economy is summarized in Table 3.5. The first column shows the average number of deaths per month in Switzerland between June and August if no heat wave occurs. There are no official projections of mortality rates available for Switzerland. Therefore, we used the population projections for 2020 (BFS, 2015b), the cohort shares in 2010 (BFS,2015a) and the average monthly mortality between 2004 and 2013 (BFS,2014a) to derive expected death per month between June and August in 2020.
We use the optimistic and the pessimistic data set derived on basis of Grizea et al. (2005) (see chapter 3.3.1) to estimate the number of excess death if a 2003 like heat wave hits Switzerland in 2020. The results are presented in column two and three of Table 3.5. In the optimistic case we estimate 1509 heat-wave-caused excess deaths in 2020. In the pessimistic case these number increases to 1581 heat-wave-caused excess deaths in 2020. For comparison, Grizea et al. (2005) estimated 975 heat-wave-caused excess deaths between June, July and August (JJA) in 2003.
Remember that neither an increase in intensity or duration of heat waves, nor the impact of demographic change has been taken into account to derive heat wave excess death for 2020. Therefore, we have to take into account that these projections are rather underestimated, because, for example, demographic trends predict an increase in the share of older cohorts compared to young.
Death per month between June and August
Fatalities in Scenario Non-Adapt from
HW EMopt HW EMpes
2010 2020 2010 2020 2010 2020
60
65+ 4328 5937 1092 1509 1143 1581
Table 3.5: Impact of a 2003-like heat wave on mortality in Scenario Non-Adapt
3.4.2.2 General Equilibrium Impacts in Scenario Non-Adapt
In a second step, we analyze general equilibrium effects that are caused by an increase in mortality if a 2003 like heat wave hits the non-adapted Swiss economy.
Therefore, we differentiate between impacts on (1) labor supply, (2) output, (3) prices (4) consumption and (5) welfare. Table 3.6 presents the impact of a heat-wave-induced excess mortality on labor supply. Given the specification of our model, effects of heat waves on labor supply depend directly on the heat wave excess mortality in working age and indirectly on changes of (reservation) wages
and income. Labor supply decreases for all age groups, independently of the use of the optimistic or the pessimistic data set for heat wave excess mortality. The higher the cohorts age, the greater the decrease in labor supply. The greatest decrease of 10% is with the most vulnerable working cohort, who is of low income, age 70 and living in suburban regions. The impact increases the higher the income of the cohort, with the exception of the youngest. The older the cohort, the greater the decrease in labor supply for cohorts of suburban, followed by urban regions.
HW EMopt HW EMpes
u s r u s r
20 1 −0.196 −0.196 −0.196 −0.214 −0.214 −0.214 3 −0.112 −0.112 −0.112 −0.121 −0.121 −0.121 5 −0.075 −0.075 −0.075 −0.081 −0.081 −0.081 30 1 −0.025 −0.017 −0.095 −0.029 −0.019 −0.110 3 −0.256 −0.321 −0.301 −0.296 −0.370 −0.347 5 −4.452 −5.071 −1.195 −5.144 −5.859 −1.380 40 1 −0.023 −0.023 −0.122 −0.030 −0.030 −0.158 3 −0.196 −0.347 −0.322 −0.254 −0.449 −0.416 5 −2.830 −4.814 −1.040 −3.658 −6.221 −1.345 50 1 −0.036 −0.040 −0.206 −0.045 −0.050 −0.257 3 −0.248 −0.521 −0.455 −0.310 −0.652 −0.570
5 −5.876 0.000 −1.302 −7.355 0.000 −1.630
60 1 −3.347 −6.750 −0.435 −4.648 −6.950 −0.440 3 −3.646 −8.031 −0.952 −4.955 −8.262 −0.960
70 1 −4.435 −6.537 0.000 −7.690 −9.994 0.000
Table 3.6: Impact of HW EM on labor supply in scenario Non-Adapt using the example of income cohorts {h = 1, 3, 5} and age {a = 20, 30, ..., 70} in percentage change
Table 3.7 presents the impact of a 2003-like heat wave excess mortality on output Y and changes in factor prices for labor (P L) and capital (P K).
The production function is of CES type. Thus, a decrease in the total labor supply with constant total capital supply reduces the production output by 0.47%, respectively 0.1% in the pessimistic case. In equilibrium, the elasticity
of substitution equals the percentage change in the capital-labor-ratio relative to the percentage change in the wage-interest-ratio; σ = (%ΔK/L)/(%Δw/r).
Because of a positive elasticity of substitution, the increase in the capital-labor-ratio results, as expected, in an increase in the wage-interest-capital-labor-ratio (see Table 3.7).
Production output is solely used for private consumption. Thus, overall private Scenario Non-Adapt
HW EMopt HW EMpes
Y −0.470 −0.102
P L 0.163 0.199
P K −0.235 −0.286
Table 3.7: Impact of a 2003-like heat wave on mortality in an optimally adapted Swiss Economy
consumption is reduced in consequence of the decrease in production. Figure 3.3 compares the impact on private consumption using the optimistic and the pessimistic data set for heat wave excess mortality.
All age wise, invulnerable cohorts (a < 60) increase their consumption by between 0.19 and 11.98%, while all age wise, strongly vulnerable cohorts (a > 60) decrease their consumption by between −0.95 and −13.85%. Besides age, the consumption adjustment depends also on the regional vulnerability. Cohorts of age 60 in rural regions increase their consumption while vulnerable ones in suburban and urban regions decrease theirs.
While vulnerable cohorts in suburban and urban regions decrease their consumption by between −3.61 and −12.01%, less vulnerable cohorts in rural regions decrease their consumption by only −0.95 to −9.01%. The consumption increase for cohorts between 30 and 50 depends also heavily on their income level. While the consumption of cohorts in the lower income group {h = 1, 2, 3}
increase their consumption by about 1%, cohorts in the highest income group (5) increase their consumption by up to 14.5% ({a = 35, h = 5, t = s}).
Figure 3.3: Impact of 2003-like HW EM on private demand for consumption in scenario Non-Adapt using the example of income cohorts {h = 1, 3, 5} of age {a = 20, 30, ..., 80} in percentage change.
Changes in labor supply and consumption have an impact on cohorts utility, which is of CES type with leisure and consumption as arguments. Premature death reduces cohort sizes and hence their sums of utilities.
Figure 3.4 presents Hicksian equivalent variation in percentage change. From it we can draw the conclusion that welfare impacts of heat waves depend predominantly on the age of the respective cohort. Cohorts at an age with a high probability to inherit capital from heat wave fatalities increase their consumption and thus their utility level. With respect to the income group, this result is the strongest for wealthy cohorts, because they received the highest share of heritage and use it to increase consumption. On the other hand, Hicksian equivalent variation of young survivors (a < 60), with a lower or middle income (h < 4) amounts to 0.12 and 1.33%. Relating to the urban form, we find suburban and urban young cohorts benefiting most, for two reasons. First, the parents of these cohorts have the highest fatality rates and second, these cohorts own already in the initial situation the highest share of capital and profit now from inheritance. In consequence of HW EM , the size of vulnerable cohorts of age 70 to 85 is reduced and their share in total population decreases.
Fatalities leave the economy and their utility from consumption and leisure no longer contribute to the cohorts utility. As a result, we find a negative Hicksian equivalent variation of between −0.04(−0.07) and −0.18%(−0.22%) in case of the
optimistic (pessimistic) data set. The decrease in welfare is higher, the higher the income group is. Vulnerable cohorts of age 60 and 65 benefit, in terms of utility, more from inheritance than they lose because of the increase in heat wave excess mortality.
Figure 3.4: Hicksian equivalent variation in scenario 1 − NA using the example of income cohorts {h = 1, 3, 5} in percentage change.
To evaluate the results from a social welfare perspective, the aggregated equivalent variation is computed as sum of the cohort-size-weighted equivalent variation of survivors, see equation 3.9.
EVScenario=X
a,h,t
"
Ma,h,tua,h,t− Na,h,tu0a,h,t Na,h,tu0a,h,t
#
(3.9)
The overall aggregated equivalent variation in the case of the optimistic (pessimistic) data set for HW EM measures −0.010%(−0.011%).
We can summarize the key results of this section as follows. If a stylized, non-adapted economy, that features the characteristics of the Swiss household structure with respect to age, region and income distribution between 2006 and 2008, is shocked with a 2003-like heat wave excess mortality, general equilibrium effects result in negative welfare impacts for the aggregated economy, although some young cohorts, especially those with high income in urban and suburban areas increase their utility. In consequence, we find next to the negative welfare
effect also a negative impact on the distribution of income.