Shinsuke Uchida and Marcella Veronesi* Abstract

Transcription

1 EXTREME WEATHER EVENTS, MORTALITY, AND ENERGY PRICES: A NATURAL EXPERIMENT FROM JAPAN Shinsuke Uchida and Marcella Veronesi* Abstract The aims of this study are to assess the impact of extreme weather events such as heat and cold waves on mortality rates in Japan; and to investigate the impact of changes in residential electricity prices on the temperature-mortality relationship through averting behavior. Our identification strategy relies on exogenous variations in residential electricity prices across space and time arising from nuclear power plants shutdowns after the Great East Japan Earthquake and the Fukushima-Daiichi nuclear power plant accident in We find that very cold hours increase mortality rates in Japan. We also find that an increase of electricity prices causes an increase of cold-related mortality, in particular, for the elderlies. Keywords: mortality, temperature, energy prices, adverting behavior, extreme events, Japan JEL Classification: I12, Q54, Q41 * Shinsuke Uchida: Graduate School of Economics, Nagoya City University. suchida@econ.nagoya-cu.ac.jp. Marcella Veronesi: University of Verona, Department of Economics, Via Cantarane 24, Verona, Italy; and Center for Development and Cooperation (NADEL), ETH Zurich, Clausiusstrasse 37, 8092, Zurich, Switzerland. marcella.veronesi@univr.it.

2 I. Introduction The Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC 2014) warns that an increase in the frequency and intensity of extreme weather events such as heat and cold waves will raise the likelihood of severe impacts on human health. This has resulted in the adoption or at least consideration of public programs that help curb the severe consequences of extreme events. However, the success of these programs also depends on whether individuals adapt or not to extreme weather events, and how they respond to changes in energy prices. During heat and cold waves, a common strategy adopted by households is the use of cooling and heating devices to mitigate the exposure to extreme temperatures. In the U.S., the diffusion of residential air conditioning (AC) reduced the mortality impact of days with mean temperature exceeding 80 F by 75 percent in the 20 th century (Barreca et al., 2016). Because energy price hikes reduce energy consumption (Ito, 2014; Auffhammer and Rubin, 2018), variation in energy prices may have a significant impact on mortality during extreme hot and cold days through the change in averting behavior. However, little is known about the effectiveness of adaptation in reducing mortality related to extreme weather events and the effect of variation in energy prices on mortality rates during heat and cold waves. The aims of this study are (i) to assess the impact of extreme weather events such as heat and cold waves on mortality rates in Japan; and (ii) to investigate the role played by residential electricity prices during extremely hot and cold days in affecting mortality rates through averting behavior. We contribute to the existing literature (Deschênes and Moretti, 2009; Deschênes and Greenstone, 2011; Barreca, 2012; Barreca et al., 2016; White, 2017) by providing additional empirical evidence on the relationship between extreme weather events and mortality outside U.S.; and new empirical evidence on the causal effect of energy prices on the temperature-mortality relationship. Our identification strategy relies on exogenous variations in electricity prices across time and space arising from the random closure of nuclear power plants at different times and regions

3 after the Great East Japan Earthquake and the Fukushima-Daiichi nuclear power plant accident in After the accident, nuclear power plant shutdowns hiked electricity prices due to an increase in imported fossil fuels. Individuals then responded by saving energy, for example, by reducing the use of AC and heating. This may have increased mortality due to the exposure to extreme weather events such as heat and cold waves. This setting creates an ideal natural experiment to investigate the effect of energy prices on the temperature-mortality relationship. We use comprehensive monthly data on residential electricity prices, mortality rates, and hourly temperature at the city level from 2007 to We employ city-by-month, city-by-year, and year-by-month fixed effects to control for potentially confounding factors. We also construct semi-parametric daily temperature variables without imposing any functional form assumptions on the temperature-mortality relationship following the aforementioned literature. We find that mortality rates in Japan significantly increase by exposure to extremely cold hours and not hot hours, in particular, for the elderlies; and that cold-related mortality rates are significantly affected by a change in electricity prices through a change in adverting behavior. The paper is organized as follows. Section II describes the residential electricity market in Japan and the impact of Fukushima Nuclear Power Plant Accident on electricity prices. Section III describes the data including our measures of weather, mortality rates, and electricity prices. Section IV present the model and results on the temperature-mortality relationship while section V on the impact of electricity prices on the temperature-mortality relationship. Section VI offers concluding remarks.

4 II. Field Setting A. Residential Electricity Market The residential electricity market in Japan has been deregulated since April Our study covers the period when regional residential electricity prices were regulated, and prices should be considered exogenous. Before 2016, the electricity market in Japan was segregated in ten regions as displayed in Figure 1. 1 In each region, an electric power company monopolized the market share. Because the Japanese government controlled electricity prices in the residential market until March 2015, residential electricity pricing systems were almost uniform across regions. 2 A household was charged a monthly electricity bill subject to nonlinear price schedules: basic charge and 3-tier energy charges based on the consumption in the previous month of 1-120kWh, kWh and over 300kWh. 3 These unit prices differ across regions subject to the electric power company s cost structure of operation, the level of investment, and the dependence on fossil-fuel based power generations (coal, liquefied natural gas (LNG), and oil) in each region. To change the rate of basic charge and/or 3-tier energy charges for residential electricity, the electricity power companies were required to submit an application for permission by Ministry of Economy, Trade and Industry (METI). Before all nuclear power plants (NPPs) in Japan were shut down in due to safety concerns, applications for price change were approved at the almost same timing among the ten electric power companies. Figure 2 shows monthly changes in unit prices of residential electricity in the electricity regions. 4 Before 2011, the unit prices did not literally differ among regions. 1 The ten regions are Hokkaido, Tohoku, Tokyo, Hokuriku, Chubu, Kansai, Chugoku, Shikoku, Kyushu, and Okinawa. 2 Non-residential electricity market had been already deregulated; market for extra-high voltage power service since 2000 and for high voltage power service since Six regions (Hokkaido, Tohoku, Tokyo, Hokuriku, Chubu and Kyushu) and the other four regions (Kansai, Chugoku, Shikoku and Okinawa) have slightly different pricing systems, where the former applies the monthly basic charge per 10A and the latter applies the minimum charge of 1-15kWh. 4 We select seven out of ten regions in Figure 2 just because our sample cities cover only these regions for estimation due to data availability. The price trend in the other three regions is qualitatively same.

5 In addition, fuel adjustment charge was introduced in The fuel adjustment charge is predetermined by the 3-month average of imported fossil fuel prices in the previous quarter. We see a hike at the beginning of 2009 in Figure 2 due to the surge in world oil prices in Finally, the renewable energy surcharge rate was introduced in The nationally uniform rate is set for the renewable energy surcharge by METI every fiscal year subject to the level of feed-in-tariff total payments. 5 FIGURE 1: ELECTRICITY MARKET IN JAPAN Source: U.S. Energy Information Administration 5 For details about the mechanism of feed-in tariff scheme for renewable energy, please refer to Agency for Natural Resources and Energy, METI (2011).

6 Monthly average price index of electricity in (2011m1=100) m1 2008m1 2009m1 2010m1 2011m1 2012m1 2013m1 2014m1 2015m1 2016m1 Hokkaido Tohoku Tokyo Chubu Kansai Chugoku Kyushu FIGURE 2. MONTHLY AVERAGE PRICE INDEX OF RESIDENTIAL ELECTRICITY BY REGION ( ) (2011=100) Source: Own elaboration using data from the Retail Price Statistics Survey by the Ministry of Internal Affairs and Communications B. Fukushima Nuclear Power Plant Accident and Residential Electricity Prices A devastating earthquake with tsunami and the consequent Fukushima nuclear power plant accident on March 11, 2011 markedly changed electricity pricing schedules across time and regions as depicted in Figure 2. Shortage of power generation from the shutdown of all the existing NPPs was mostly replaced by increasing coal/gas/oil-fired power generation. 6 The electricity power companies had to resume operations of old and idled coal/gas/oil-fired power generators as well as to increase import of fossil fuels. 7 6 At the time of the Fukushima accident, 37 out of 54 reactors were in operation in Japan (The Independent Investigation Commission on the Fukushima Nuclear Accident, 2014). Shutdown of all but four reactors in Kansai and Kyushu has continued as of January To make the matter worse, oil price had been peaked at more than $100/barrel in 2012 due to instability in the Middle East (Iran, Syria, Egypt, Libya, and Iraq). In general, per unit operation cost of coal/gas/oil-fired power plants is much higher than the one of nuclear power plants, so the net effect of substitution from nuclear to fossil fuels is negative.

7 Because the dependence on nuclear power before the Fukushima accident varied across regions (ranging from 0 percent to 56 percent of power supply during ), the substitution of fossil fuels for nuclear power differed by region (Cho et al., 2016). Moreover, NPP owned companies invested higher safety/security systems to resume NPP operations under tighter regulations. These resulted in a non-uniform and rapid increase in electricity prices across regions after As seen in Figure 2, Hokkaido, Kansai, and Tokyo regions have experienced several large price jumps. 8 Figure 3 shows that their energy dependence on NPPs was the largest before Tokyo Electric Power Corporation, which owns Fukushima Daiichi Nuclear Power Plant, also stated that compensation for the damages caused by the Fukushima disaster pushed up their costs (Cho et al., 2016). In contrast, the price schedules did not increase as much in Chugoku region where smaller share of electricity was generated by NPPs prior to FIGURE 3. COMPOSITION OF ELECTRICITY PRODUCTION IN TEN ELECTRICITY REGIONS Source: Chan et al. (2017) According to METI (2015), average annual costs of the substitution amount to about 3.1 trillion yen (0.65% of GDP) in Hokkaido increased the electricity price by 7.73% in September 2013, 12.43% in November 2014, and 15.33% in April Similarly, Kansai raised the price electricity by 9.75% in May 2013, 4.62% in June 2015, and 8.36% in October 2015.

8 The dramatic price change did influence residential energy consumption. Figure 4 plots annual average of residential electricity consumption per capita in each electricity region. 9 Residential electricity consumption dropped after 2011, particularly in when prices jumped as shown in Figure This phenomenon is consistent with the results of household surveys conducted by the Ministry of the Environment in the summer and winter of year 2012 (2012a; 2012b), where an average respondent reduced electricity consumption by 1-8% depending on the region. The reduction rate was higher in areas with more increase in electricity prices. Also, the reduction rate was higher in the winter than in the summer. Moreover, the surveys show that the main actions taken by the survey respondents to save electricity are a reduction in hours of AC use and/or changes in AC temperatures. 11 These facts cast a light on our hypothesis that energy prices affect the temperature-mortality relationship by refraining from averting behavior. 00 NATIONAL 01 Hokkaido 02 Tohoku 03 Tokyo Chubu 05 Kansai 06 Chugoku 07 Kyushu FIGURE 4. DYNAMICS OF ANNUAL AVERAGE RESIDENTIAL ELECTRICITY CONSUMPTION PER CAPITA BY REGION IN (2010=100) Source: Own elaboration using data from the Electricity Statistics Information by the Federation of Electric Power Companies of Japan. 9 Aggregate electricity consumption data are available at region level. We then divide them by the size of respective population. 10 In April 2014, Japanese Government raised ad valorem tax for consumption goods from 5% to 8%, which should also affect energy price (retail) and consumption. Correlation between annual average price and annual average consumption per capita is very high about -0.9 in every region in In Japan, people mainly use AC as well as some other electric devices for heating. Exception exists in the northern Japan where kerosene or gas heaters is a majority.

9 III. Data Description We explore the temperature-price-mortality relationship by using data on i) mortality rates; ii) weather variables; and iii) residential electricity prices. The unit of analysis is city-year-month for years A. Mortality Data. Monthly mortality counts data at the municipality level come from the Survey on Population Dynamics by Ministry of Health, Labor and Welfare. 12 Data are available for the 21 largest municipalities in Japan, so-called designated cities. A municipality with a population greater than 500,000 can be designated by government ordinance. Total designated cities are 13 until 2002, and increased to 21 by 2012 as shown in Figure 5. They are located in seven of the ten electricity regions, except for Hokuriku, Okinawa, and Shikoku regions. Chubu, Chugoku, Hokkaido, Kansai, Kyushu, Tohoku, and Tokyo regions include 3, 2, 1, 4, 3, 2, and 6 designated cities, respectively. FIGURE 5. LOCATIONS OF 21 DESIGNATED CITIES These data include information on the demographic characteristics of the decedent such as gender and age groups. This information enables us to estimate heterogeneous health impacts of 12 Part of the data were provided upon special tabulation requests.

10 temperature and electricity prices. Mortality counts data are combined with city population data to compute mortality rates (per 100,000 population). We use annual population of the designated cities available from Statistics Bureau of the Ministry of Internal Affairs and Communications. B. Weather Data. We use hourly weather information from the Japan Meteorological Agency. All but five designated cities have their weather stations located in the city center. For Kawasaki, Kitakyushu, Saitama, Sagamihara, and Sakai data are substituted by the nearest stations in neighbor municipalities (ranging from 9-28 km distances). A key variable for our analysis is hourly average temperature. We follow Deschênes and Greenstone (2011) and Barreca et al. (2016) to construct temperature bins to approximate the distribution of temperatures. Figure 6 illustrates the annual average distribution of hourly average temperature across eight temperature bins (< 0, 0-4, 5-9, 10-14, 24-29, >30 C). Each bar represents the average number of hours per year in each temperature bin divided by 24 hours and weighted by the total population in a city-year. Figure 7 illustrates the same figure across the seven electricity regions considered in our analysis. Variations in hourly temperature are observed in both tails of the temperature distribution. In contrast to the literature, this temporally disaggregated data may reveal a masked temperature-mortality relationship, particularly by focusing on the temperature difference between day and night time. Figure 8 shows notable differences in the coldest and hottest temperature bins. These can account for heterogeneous temperature effects on mortality within a city. We also collected hourly data on precipitation, daylight, average humidity, and average wind speed. Table 1 shows the relationship between mortality rates and temperatures across regions.

11 FIGURE 6: DISTRIBUTION OF HOURLY TEMPERATURES ( ) Note: The figure represents the number of days per year with hourly temperature in one of the bins <0, 0-4, 5-9, 10-14, 15-19, 20-24, 25-29, and >30 C in the seven electricity regions included in our analysis (Chubu, Chugoku, Hokkaido, Kansai, Kyushu, Tohoku, and Tokyo). Number of days per year are calculated as the number of hours in a bin divided by 24 hours and weighted by the total population in a city-year. FIGURE 7. DISTRIBUTION OF HOURLY TEMPERATURES BY ELECTRICITY REGION ( ) Note: The figure refers to the seven electricity regions included in our analysis (Chubu, Chugoku, Hokkaido, Kansai, Kyushu, Tohoku, and Tokyo). Number of days per year are calculated as the number of hours in a bin divided by 24 hours and weighted by the total population in a city-year.

12 FIGURE 8. DISTRIBUTION OF HOURLY TEMPERATURES OF DAYTIME AND NIGHTTIME ( ) Note: The figure refers to the seven electricity regions included in our analysis (Chubu, Chugoku, Hokkaido, Kansai, Kyushu, Tohoku, and Tokyo). Number of hours are calculated as the number of hours in a bin divided by 24 hours and weighted by the total population in a city-year. TABLE 1: DESCRIPTIVE STATISTICS ON MORTALITY RATE AND TEMPERATURE EXTREMES All-age Mortality Rate Number of Days per Year with Hourly Temperature Total Male Female <0 0-4 C >30 Total By Electricity Region 01 Hokkaido Tohoku Tokyo Chubu Kansai Chugoku Kyushu Note: Statistics are weighted by the total population in a city-year. C. Residential Electricity Prices Data Monthly residential electricity prices are obtained from the Retail Price Statistics Survey by the Ministry of Internal Affairs and Communications. As described above, these are uniform for cities within the same electricity region. The basic charge and the 3-tier energy charges as well as the fuel adjustment charge and the renewable energy surcharge rate are separately available. We compute the average (unit) price of electricity by assuming that a representative household consumes

13 electricity by 441kWh per month with a 50A contract (we follow this procedure defined by the Japanese government s statistical office). IV. Econometric Models and Identification Strategy A. The Temperature-Mortality Relationship We estimate the following equation to elicit the causal effect of temperature on mortality rates by following Deschênes and Greenstone (2011) and Barreca et al. (2016): ln$m &'( ) = - α - T &'(- + R &'( β + γd &'( + ρ &( + π &' + θ '( + ε &'( (1) where ln$m &'( ) is the logarithm of the monthly mortality rate in city c, in year y, and month t (number of deaths per 100,000); T &'(- represents the temperature variables defined as the number of hours in city c, year y, and month t where the temperature is in bin i < 0, 0-4, 5-9, 10-14, 24-29, >30 degrees Celsius with as the excluded category. This implies that the impact of the hourly temperature on the monthly mortality rate is constant within 5 C intervals, and that we model daily temperature semi-parametrically without imposing any functional form assumptions on the effect of temperature on mortality. We also investigate the possibility of a dynamic relationship between weather and mortality rates and estimate our models with both current and prior month temperature to account for mortality displacement following Deschênes and Greenstone (2011) and Barreca et al. (2016). If we neglect this effect our estimates might overstate the effect of temperature on mortality due to harvesting, that is if sick individuals would have died anyway even in the absence of the extreme weather events. The event such as very cold or hot hours may have simply anticipated mortality of individuals whose health is already compromised. On the other hand, estimates might be underestimated if we do not account for mortality displacement because some diseases such as respiratory diseases take time to develop, and so the effect of extremes temperature on mortality may be delayed of days or weeks.

14 Equation (1) also includes a vector of two dummy variables (Rcyt) equal to one if monthly precipitation is less than the 25 th or more than the 75 th percentile of the average monthly precipitation in a given city-month, respectively. These variables allow us to control for unusually high or low city-by-month precipitations. The variable dcyt is a dummy variable that accounts for extreme death counts due to the earthquake that occurred in Sendai province in March In addition, we include in equation (1) city-by-month (ρ &( ), city-by-year (π &' ), and year-bymonth (θ '( ) fixed effects. City-by-month (ρ &( ) fixed effects account for differences in seasonal mortality and fixed city-by-month unobserved factors potentially affecting mortality rates (e.g., migration; seasonal employment); city-by-year (π &' ) fixed effects adjust for permanent city-by-year unobservable determinants of mortality rates (e.g., geographic factors, age pyramid and income distribution; hospital quality); and year-by-month (π &' ) fixed effects control for time varying factors that are common to all cities (e.g., national business cycles). The last term (ε &'( ) in equation (1) represents the idiosyncratic error term. All regressions are weighted by city-year population and clustered at the city level to account for serial correlation of weather and mortality within a city. In summary, our empirical strategy to identify the effect of temperature on mortality rates relies on random variations in the weather distribution for a given city and month within a year. B. Residential Electricity Prices and the Temperature-Mortality Relationship In this section, we describe the econometric model used to study the effect of residential electricity prices on the temperature-mortality relationship. We add residential electricity prices and their interaction with temperature in equation (1), and estimate the following equation: ln$m &'( ) = : α - T &'(- + R &'( β + γd &'( - + δ ln$p &',(>? ) + - λ - T &'(- ln$p &',(>? ) (2) + ρ &( + π &' + θ '( + ε &'(

15 where ln$m &'( ) is the logarithm of the monthly mortality rate in city c, in year y, and month t; T &'(- is the number of hours in city c, year y, and month t where the temperature is in bin i < 0, 0-4, 5-9, 10-14, 24-29, 30 plus C with C as the excluded category; ln$p &',(>? ) is the logarithm of the average residential electricity price in city c in year y and month (t-k), with k = 1, 2 when we investigate the impact of prices of one or two months before. We use one or two months lags in prices because adverting behavior is more likely to be affected by the price of the previous month or two months before. Electricity bills received at time t include the price of the month before so mortality at time t can be affected by a change in electricity consumption at the same time t that depends on prices at (t-1), or by a change in consumption in the previous month that depends on prices at (t-2). We expect that realizing during very hot or cold days that electricity prices have been raised increases mortality by a decrease in adverting behavior, for example, by using less air conditioning and heating, and so individuals are more likely to suffer from heat or cold. This implies that we expect a positive sign of the interaction term coefficient λ between temperature and price. Energy prices are expected not to affect mortality in between extreme temperatures. In other words, the causal relationship between prices and mortality arises only in the presence of extreme temperatures, through the channel of adverting behavior. All remaining variables are defined as in equation (1), and as before, we include city-bymonth (ρ &( ) fixed effects to account for differences in seasonal mortality, use of alternative energy sources, and seasonality of prices if present although given the highly regulated market it is unlikely to experience price seasonality, and as before, fixed city-by-month unobservable factors potentially affecting mortality rates such as seasonal employment, migration and epidemics such as influenza; city-by-year (π &' ) fixed effects to adjust for permanent city-by-year unobservable determinants of mortality rates such as geographic factors, age pyramid, differences in income distribution across cities, hospital quality, and energy-saving campaigns conducted almost every year after 2011; and year-by-month (π &' ) fixed effects control for time varying factors that are common to all cities such

16 as national information policies on energy use, national business cycles, and efficiency of energy appliances. All regressions are weighted by city population and standard errors are clustered at the city level. Our empirical strategy relies first, on random variations in the weather distribution for a given city and month within a year; and second, on the fact that in our period of analysis residential electricity prices are not endogenous. The Japanese residential electricity market was a regional monopoly highly regulated by the central government in our period of analysis. Electricity prices could not be raised without the approval of the Ministry of Economy, Trade and Industry. Last but not least, we also exploit the exogenous variations across space and time in residential electricity prices arising from nuclear power plants shutdowns after the Great East Japan Earthquake and the Fukushima-Daiichi nuclear power plant accident in 2011 as described in the section on the field setting. The magnitude of the change in electricity prices depends on the initial proportion of nuclear-powered generation capacity as well as the choice of generation replacement. Such exogeneous changes allow us to identify the impact of adverting behavior on mortality during days with extreme temperatures. V. Results A. The Temperature-Mortality Relationship We start by reporting results from the estimation of equation (1). Figure 9 presents cumulative dynamic estimates that account for short and long run effects of temperature on mortality. This figure shows that the impact of temperature is significant and positive during very cold hours (< 5 C) while insignificant when temperature is above 4 C. The point estimates indicate that the effect of an additional hour below 5 C significantly increases mortality rates by 0.03 percent with respect to the effect of an additional hour in the 15 C-19 C range. The estimates associated with temperature above 4 C are smaller in magnitude and not significant although the associated coefficients remain positive.

17 When we consider the effects of temperature on mortality rates by age groups (figures 9a- 9d) we find that the most affected group is elderlies older than 65 years and that the effect is the same as in the full sample; while when we study the temperature-mortality relationship by gender (figures 10a-10b), we find that females mortality is strongly and significantly affected by temperature extremes, and in particular, by temperature below 4 C, while males mortality only weakly by temperature 0-4 C at the 10 percent significance level. The results indicate that the physically vulnerable age group is at the highest risk during the exposure to extreme cold temperatures. FIGURE 8: CUMULATIVE DYNAMIC ESTIMATES OF TEMPERATURE-MORTALITY RELATIONSHIP Notes. Data refer to the period The dependent variable is the logarithm of monthly mortality rate. Specifications include city-month, city-year, year-month fixed effects; the number of hours where the temperature is in bin < 0, 0-4, 5-9, 10-14, 24-29, 30 plus C with C as the excluded category; two rain dummy variables; and a dummy variable for extreme death counts due to the earthquakes in year The temperature exposure window is two months. Regressions are weighted by city population. Standard errors are clustered at the city level.

18 FIGURE 9A: AGE 0-4 FIGURE 9B: AGE 5-19 FIGURE 9C: AGE FIGURE 9D: AGE 65 PLUS FIGURE 9: CUMULATIVE DYNAMIC ESTIMATES OF TEMPERATURE-MORTALITY RELATIONSHIP BY AGE GROUP Notes. Data refer to the period The dependent variable is the logarithm of monthly mortality rate. Specifications include city-month, city-year, year-month fixed effects; the number of hours where the temperature is in bin < 0, 0-4, 5-9, 10-14, 24-29, 30 plus C with C as the excluded category; two rain dummy variables; and a dummy variable for extreme death counts due to the earthquake in year The temperature exposure window is two months. Regressions are weighted by city population. Standard errors are clustered at the city level.

19 FIGURE 10A: MALE FIGURE 10B: FEMALE FIGURE 10: CUMULATIVE DYNAMIC ESTIMATES OF TEMPERATURE-MORTALITY RELATIONSHIP BY GENDER Notes. Data refer to the period The dependent variable is the logarithm of monthly mortality rate. Specifications include city-month, city-year, year-month fixed effects; the number of hours where the temperature is in bin < 0, 0-4, 5-9, 10-14, 24-29, 30 plus C with C as the excluded category; two rain dummy variables; and a dummy variable for extreme death counts due to the earthquake in The temperature exposure window is two months. Regressions are weighted by city population. Standard errors are clustered at the city level. B. Residential Electricity Prices and the Temperature-Mortality Relationship We first present results on the effect of residential electricity prices on the temperature-mortality relationship by estimating equation (2) for the full sample. Figure 11 shows the estimated impacts of electricity prices by plotting the coefficients l associated with the interaction terms of equation (2) between prices and temperature bins. The specification includes current and previous month temperature bins to capture the dynamic effect of temperature on mortality. Figures 11a-11b show the estimated impacts of electricity prices of the previous month (t-1) and two months before (t-2). We find that the coefficients of the interaction terms between electricity prices and number of hours below 0 C are positive and significant at the 5 percent level as expected. A price increase in either time periods has a sizable and statistically significant detrimental effect on cold-related mortality. A 10 percent increase in residential electricity prices increases mortality due to very cold hours by 0.01 percent. However, we do not find an effect of electricity prices on mortality during very hot hours.

20 In addition, we investigate whether these effects differ by age groups and gender. We use the specification that includes electricity prices at time t-2. Our results are unchanged if we use prices at t-1. Figure 12 shows that the elderlies are a particular vulnerable group. The impact of electricity prices on the temperature-mortality relationship is statistically significant only for individuals older than 65 years and during very cold hours. Interestingly, the magnitude of the effect is similar to previous estimates for the full sample. When we distinguish between men and women, Figure 13 shows that only female mortality due to temperature below 0 C is significantly affected by a change in electricity prices. A 10 percent increase in electricity prices is associated with an increase in female mortality due to very cold hours by percent. These results indicate that the physically vulnerable age group is also economically vulnerable to the exposure to extreme cold temperatures. This can be explained by the fact that the elderlies likely have price elastic demand for AC, because people over 65 are mostly retired and thus have limited income from the pension system. In addition, they are more sensitive to coldness by losing thermoregulatory ability.

21 FIGURE 11A: PRICES AT (T-2) FIGURE 11B: PRICES AT (T-1) FIGURE 11: THE IMPACT OF RESIDENTIAL ELECTRICITY PRICES ON THE TEMPERATURE-MORTALITY RELATIONSHIP Notes. Data refer to the period The figure shows the estimated l coefficients associated with the interaction terms of residential electricity prices at time t-2 (Figure 9a), t-1 (Figure 9b) with the eight temperature bins variables of equation (2). The dependent variable is the logarithm of monthly mortality rate. Specifications include city-month, cityyear, year-month fixed effects; the number of hours where the temperature is in bin < 0, 0-4, 5-9, 10-14, 24-29, 30 plus C with C as the excluded category; the logarithm of the monthly average electricity price; two rain dummy variables; and a dummy variable for extreme death counts due to the earthquake in The temperature exposure window is two months. Regressions are weighted by city population. Standard errors are clustered at the city level. FIGURE 12A: AGE 0-4 FIGURE 12B: AGE 5-19

22 FIGURE 12C: AGE FIGURE 12D: AGE 65 PLUS FIGURE 12: THE IMPACT OF RESIDENTIAL ELECTRICITY PRICES ON THE TEMPERATURE-MORTALITY RELATIONSHIP BY AGE GROUP Notes. Data refer to the period The figure shows the estimated l coefficients associated with the interaction terms of residential electricity prices at time t-2 with the eight temperature bins variables of equation (2). The dependent variable is the logarithm of monthly mortality rate. Specifications include city-month, city-year, year-month fixed effects; the number of hours where the temperature is in bin < 0, 0-4, 5-9, 10-14, 24-29, 30 plus C with C as the excluded category; the logarithm of the monthly average electricity price; two rain dummy variables; and a dummy variable for extreme death counts due to the earthquake in The temperature exposure window is two months. Regressions are weighted by city population. Standard errors are clustered at the city level.

23 FIGURE 13A: MALE FIGURE 13B: FEMALE FIGURE 13: THE IMPACT OF RESIDENTIAL ELECTRICITY PRICES ON THE TEMPERATURE-MORTALITY RELATIONSHIP BY GENDER Notes. Data refer to the period The figure shows the estimated l coefficients associated with the interaction terms of residential electricity prices at time t-2 with the eight temperature bins variables of equation (2). The dependent variable is the logarithm of monthly mortality rate. Specifications include city-month, city-year, year-month fixed effects; the number of hours where the temperature is in bin < 0, 0-4, 5-9, 10-14, 24-29, 30 plus C with C as the excluded category; the logarithm of the monthly average electricity price; two rain dummy variables; and a dummy variable for extreme death counts due to the earthquake in The temperature exposure window is two months. Regressions are weighted by city population. Standard errors are clustered at the city level. VI. Conclusions Along with the progress of global warming, many studies have shown that extreme weather events such as heat and cold waves have non-negligible negative effects on human health (Deschênes and Moretti, 2009; Deschênes and Greenstone 2011; Barreca 2012; Barreca et al., 2016; White, 2017). Mortality rates during the summer and winter are rising with an increase of very hot and cold days. Quantitative evidences of such influences are needed for planning specific concrete global warming countermeasures. Existing studies have analyzed relevant medical interventions between climatic conditions and mortality, but few investigate the impact of climate on people s adaptive behavior. For instance, the use of air-conditioning is recommended as an effective measure to prevent heat stroke. If adaptation alleviates the effects of extremely hot weather, an analysis that neglects such behavior would underestimate the influence of extreme weather events on health (Barreca et al. 2016).

24 In this study, we estimate the causal effect of electricity prices on the temperature-mortality relationship through adaptive behavior. Our identification strategy relies on exogenous variations across space and time in electricity prices arising from nuclear power plants shutdowns after the Great East Japan Earthquake and the Fukushima-Daiichi nuclear power plant accident in We find that exposure to very cold temperatures increases mortality rates, particularly for the elderlies, and cold-related mortality increases with an increase of electricity prices through a change in adverting behavior. This indicates the importance of the interaction of health and economic policies to support physically and economically vulnerable groups to the exposure of extreme weather events.

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