The Impact of Temperature on Mortality …
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urban, with 91% living in towns bigger than 2000 inhabitants, and half of the total
population living in just 8 cities. Therefore, these 61 weather stations included in the
analysis provide coverage to approximately 82% of the country’s population.
Daily station data had to be spatially interpolated at the municipal level to construct
monthly weather exposure variables. Following a common approach in the literature,
square inverse distance weights were calculated from each municipality geographical
centroid to each station within a 100 km radius (Hanigan et al. 2006). Thus, the
interpolated weather variables are simply the weighted average of the station records
within that radius. Limiting the cutoff distance 100 km was necessary to balance
the number of municipalities included in the study and minimizing the potential
measurement error introduced by using distant stations. Similar cutoff levels have
been used in empirical estimates for the USA (Barreca 2012; Barreca and Shimshack
2012; Barreca et al. 2016; Deschênes and Greenstone 2011; Ranson 2014).
Impact of Extreme Temperatures on Mortality
Panel A of Table 1 presents summary statistics of interpolated daily mean, maximum,
and minimum temperatures for the 322 municipalities included in the study, stratified
by census region, over the 2004–2010 period. It can be observed that the lower temperature records correspond to municipalities in Cuyo and Pampean regions—recall
Patagonia has been excluded due to lack of complete weather station records. Minimum temperatures over these regions are on average 5.9 and 5.2
◦ C lower than in the
warmer northeast region. As expected, municipalities in northern regions experience
higher maximum temperatures, with records between 3.9 and 4.9
◦ C higher than the
region with the lowest maximum temperatures.
Summary statistics for monthly mortality rates between age groups are reported
in Panel B and by region in Panel C of the same table. Average mortality rates are
10.8 per 100,000 inhabitants for children under 5 year of age, 6.7 for people between
5 and 44 years of age, 47 for adults between 44 and 65, and 325 for older adults.
Consequently, one should expect to observe larger impacts of extreme temperatures
among these two last groups, given that they appear to the most vulnerable people.
Looking at different regions, higher mortality seems to be correlated with higher
daily mean temperatures, although one should be cautious to infer causation since
confounding factors, for instance income levels, could be also correlated with climate.
Notwithstanding, the identification strategy described in the previous section allows
controlling for these confounding factors by including a series of fixed effects.
Figure 1 presents the estimated exposure impacts, and their respective 95% confidence intervals, computed by nonparametric regressions between mortality rates
and temperature at the municipal level, controlling for precipitation levels, and fixed
effects by month and municipality by month. The results must be interpreted as the
relative impact of temperature (daily mean, maximum, or minimum) over annual
mortality in percentage points (pp) due to an additional day in a given month with
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urban, with 91% living in towns bigger than 2000 inhabitants, and half of the total
population living in just 8 cities. Therefore, these 61 weather stations included in the
analysis provide coverage to approximately 82% of the country’s population.
Daily station data had to be spatially interpolated at the municipal level to construct
monthly weather exposure variables. Following a common approach in the literature,
square inverse distance weights were calculated from each municipality geographical
centroid to each station within a 100 km radius (Hanigan et al. 2006). Thus, the
interpolated weather variables are simply the weighted average of the station records
within that radius. Limiting the cutoff distance 100 km was necessary to balance
the number of municipalities included in the study and minimizing the potential
measurement error introduced by using distant stations. Similar cutoff levels have
been used in empirical estimates for the USA (Barreca 2012; Barreca and Shimshack
2012; Barreca et al. 2016; Deschênes and Greenstone 2011; Ranson 2014).
Impact of Extreme Temperatures on Mortality
Panel A of Table 1 presents summary statistics of interpolated daily mean, maximum,
and minimum temperatures for the 322 municipalities included in the study, stratified
by census region, over the 2004–2010 period. It can be observed that the lower temperature records correspond to municipalities in Cuyo and Pampean regions—recall
Patagonia has been excluded due to lack of complete weather station records. Minimum temperatures over these regions are on average 5.9 and 5.2
◦ C lower than in the
warmer northeast region. As expected, municipalities in northern regions experience
higher maximum temperatures, with records between 3.9 and 4.9
◦ C higher than the
region with the lowest maximum temperatures.
Summary statistics for monthly mortality rates between age groups are reported
in Panel B and by region in Panel C of the same table. Average mortality rates are
10.8 per 100,000 inhabitants for children under 5 year of age, 6.7 for people between
5 and 44 years of age, 47 for adults between 44 and 65, and 325 for older adults.
Consequently, one should expect to observe larger impacts of extreme temperatures
among these two last groups, given that they appear to the most vulnerable people.
Looking at different regions, higher mortality seems to be correlated with higher
daily mean temperatures, although one should be cautious to infer causation since
confounding factors, for instance income levels, could be also correlated with climate.
Notwithstanding, the identification strategy described in the previous section allows
controlling for these confounding factors by including a series of fixed effects.
Figure 1 presents the estimated exposure impacts, and their respective 95% confidence intervals, computed by nonparametric regressions between mortality rates
and temperature at the municipal level, controlling for precipitation levels, and fixed
effects by month and municipality by month. The results must be interpreted as the
relative impact of temperature (daily mean, maximum, or minimum) over annual
mortality in percentage points (pp) due to an additional day in a given month with