The Review of Economics and Statistics
VOL. XCINOVEMBER 2009

NUMBER 4

EXTREME WEATHER EVENTS, MORTALITY, AND MIGRATION
Olivier Deschênes and Enrico Moretti*
Abstract—We estimate the effect of extreme weather on life expectancy in
the United States. Using high-frequency data, we find that both extreme
heat and cold result in immediate increases in mortality. The increase inmortality following extreme heat appears mostly driven by near-term
displacement, while the increase in mortality following extreme cold islong lasting. We estimate that the number of annual deaths attributable tocold temperature is 0.8% of average annual deaths in our sample. The
longevity gains associated with mobility from the Northeast to the Southwest account for 4% to 7% of the total gains in life expectancy experienced by the U.S. population over the past thirty years.

I.

IntroductionT

HROUGH the twentieth century, the U.S. population
experienced an unprecedented increase in life expectancy. The economic value of such an increase is enormous,
exceeding, by some calculations, the value of the growth in
all nonhealth goods and services (Nordhaus, 2002). The
determinants of the increase in life expectancy are numerous and complex, but it appears that economic growth,
public health measures, and, especially, science and technology were important determinants. Cutler, Deaton, and
Lleras-Muney (2006) provide a recent survey of the importance of the various determinants and their interplay.1
In this paper, we focus on the relationship between
weather and mortality in the United States. Specifically, we
estimate the effect of episodes of extreme heat and extreme
cold on longevity. We use these estimates to provide new
evidence on the underlying causes of long-run increases inlife expectancy experienced by the U.S. population over the
past several decades.2
Extreme weather events generate enormous public interest. Each summer, the popular press devotes significant
coverage to the impact of heat waves on mortality. Heatwaves are claimed to kill scores of people, especially among
the poor and the elderly. Recent examples include the 2006
Received for publication June 19, 2007. Revision accepted for publication February 27, 2008.
We thank seminar participants at the Princeton University Industrial
Relations Labor Lunch, UC Davis, and UC Santa Cruz for useful suggestions. We also thank the editor and two anonymous referees for usefulcomments. Mariana Carrera and Paul McCathcart provided excellent
research assistance.
* University of California, Santa Barbara, and NBER (Deschênes);
University of California, Berkeley, and NBER (Moretti).
1 See Costa (2003) for historical evidence.
2 While considerable attention has been devoted to effect of weather on
economic outcomes in developing countries (for example, Miguel, 2005;
Acemoglu, Simon, & Robinson, 2003; Oster 2004), less attention has been
devoted to the effect of weather in the United States.

heat wave in California (400 deaths), the 2005 heat wave inArizona (100 deaths), and the particularly deadly heat wave
in France in 2003, which, according to the French National
Institute of Health and Medical Research, caused 18,000
deaths. Cold waves are also claimed to increase mortality.
The clamor associated with these events sometimes resultsin drastic and costly policy changes. For example, following
the 1995 heat wave, which reportedly caused 800 deaths in
Chicago, Mayor Richard M. Daley put in place an articulated policy of response to extreme weather events that
includes the mobilization of thousands of emergency personnel to contact, provide supplies to, and, in some cases,
relocate elderly citizens.3
While it is clear that mortality spikes in days of extreme
hot or cold temperature, the significance of those deaths interms of reduction in life expectancy is much less clear. The
number of deaths caused by extreme temperatures on a given
day could be compensated for by a temporary fall in mortality
in the subsequent days or weeks, if extreme temperatureprincipally affects individuals whose health is already compromised. This could happen if extreme temperature precipitates
the health condition of individuals who are already weak andwould have died even in the absence of the shock. In this case,the only effect of the weather shock is to change the timing ofmortality by a few days or weeks, but not the number of deathsin the longer run. Such temporal displacement is sometimes
referred to as a harvesting effect. Thus, the excess mortality
observed on cold and hot days does not necessarily imply
significant permanent reductions in life expectancy.4
Unlike much of the previous literature, our estimates ofthe effect of extreme weather events on mortality allow a
flexible dynamic relationship between weather shocks andmortality, and therefore account for the possibility of nearterm mortality displacement. We base our analysis on data
3 In addition to the immediate impact of extreme weather on mortality,
there is now increasing concern that higher temperatures and incidence of
extreme weather events caused by global warming could create major
public health problems in the future. A growing literature analyzes thatand related questions (Deschênes & Greenstone, 2007; Kalkstein, 1993;
Tol, 2002). In this paper, however, we leave these issues aside and focus
on the impact of extreme temperature on realized longevity.
4 On the other hand, the opposite may also be true. Consider, forexample, the case where unusually low temperature today results inincreased mortality over the next few days or weeks, because some
respiratory conditions take time to fully develop and spread. This delayed
response would imply that the long-run effect of extreme weather is larger
than the short-run effect.

The Review of Economics and Statistics, November 2009, 91(4): 659–681© 2009 by the President and Fellows of Harvard College and the Massachusetts Institute of Technology

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that include the universe of deaths in the United States overthe period 1972–1988. We match each death to weather
conditions on the day of death and in the county of occurrence. The use of high-frequency data and the fine geographical detail allow us to estimate with precision theeffect of cold and hot temperature shocks on mortality, as
well as the dynamics of such effects.
Our results point to widely different impacts of cold andhot temperature on mortality. Consistent with accounts inthe media, we find that hot temperature shocks are indeed
associated with a large and immediate spike in mortality inthe days of the heat wave. As expected, this effect isparticularly large for elderly individuals. Remarkably, however, almost all of this excess mortality is explained bynear-term displacement. In the weeks that follow a heatwave, we find a marked decline in mortality hazard, which
completely offsets the increase during the days of the heatwave. As a consequence, there is virtually no lasting impactof heat waves on mortality.5
In contrast, we find that the cold temperature days have asignificant and long-lasting impact on mortality rates. Coldwaves are associated with an immediate spike in mortality
in the days of the cold wave, but there is no offsetting
decline in the weeks that follow. The cumulative effect of
one day of extreme cold temperature during a thirty-day
window is an increase in daily mortality by as much as 10%.
As such, the deaths attributable to cold temperature represent significant reduction in life expectancy. This impact ofcold weather on mortality is significantly larger for females
than for males. For both genders, the effect is mostly
attributable to increased mortality due to cardiovascular andrespiratory diseases. When we stratify by income, we findthat the impact of extreme cold temperature is significantly
larger for males living in low-income areas. Not surprisingly, infants and older adults are more affected by cold
temperature than are prime-age adults.6
The aggregate magnitude of the impact of extreme coldon mortality in the United States is large. We estimate thatthe number of annual deaths attributable to extreme cold
temperature in the white population is 14,380, or almost 360
deaths per cold day. This roughly corresponds to 0.8% of
average annual deaths in the United States during thesample period. We interpret this figure as a remarkably large
number: it exceeds the annual deaths due to leukemia,
homicide, and chronic liver disease or cirrhosis. The overall
impact on longevity is substantial: the average person who
died because of cold temperature exposure lost in excess often years of potential life.
Of course, there are sizable differences across cities in theincidence of cold-related deaths. Minneapolis, Detroit,
Cleveland, and Chicago are the most affected, with esti5 Of course, every death is harvesting, because we all eventually die. The

point here is that in a heat wave, some individuals die only a few days
earlier than they would have, not a few months or years earlier.
6 In contrast, cold temperature reduces mortality for young adults (aged
20–34) through a marked reduction in motor vehicle accident fatalities.

mates ranging from 1.4% to 3.2% of annual deaths that
could be delayed by changing the exposure to extreme colddays.
Our findings have important implications for explaining
improvements in the life expectancy of the U.S. population.
We estimate that a significant fraction of the increase inlongevity experienced by the U.S. population over the past
thirty years can be attributed to reduced exposure to cold
days induced by geographical mobility. Geographical mobility affects longevity because it modifies the exposure ofindividuals to extreme temperatures. As a whole, the U.S.
population has moved from cold northeastern states to warm
southwestern states. For each individual in the United States
who lives in a state different from the state of birth, we
compare the exposure in the state of residence with thecounterfactual exposure that that individual would haveexperienced in the state of birth.
We calculate that each year 4,600 deaths are delayed by
the changing exposure to cold temperature due to mobility.
As a consequence, the average individual experiences anincrease in longevity of 0.008 to 0.015 years per calendar
year as a result of the lower exposure to cold weather. We
compare this figure to the increase in longevity experienced
in the United States over the past thirty years. Our estimatesindicate that 3% to 7% of the gains in longevity experienced
by the U.S. population over the past three decades are dueto the secular movement toward warmer states in the Westand the South, away from the colder states in the North.
This evidence on mobility-induced changes to cold weather
exposure identifies an important but previously overlooked
explanation for increased longevity in the United States.
Finally, we test whether mobility decisions of individuals
are correlated with the health benefits associated with avoiding extreme cold. We find that the probability of moving toa state that has fewer days of extreme cold is higher for theage groups that are predicted to benefit more in terms oflower mortality compared to the age groups that are predicted to benefit less. While this finding is consistent with amodel of rational mobility, there are many unobserved
determinants of mobility that we cannot account for, andtherefore this correlation does not necessarily have a causal
interpretation.
In the next section, we review the existing literature on
the link between extreme weather and mortality. In section
III, we describe the data. In section IV, we present theestimates of the effect of heat and cold waves on mortality.
In section V, we quantify the effect of cold waves on
longevity and the effect of geographical mobility on longevity. Section VI concludes.
II.

Background

A. Existing Literature

The relationship between excessively high or low temperature and mortality has been well documented since theEXTREME WEATHER EVENTS, MORTALITY, AND MIGRATION

early 1900s (see Grover, 1938, for an early example),though most of the emphasis is on the immediate effect of
extreme heat. For example, Curreiro et al. (2002) estimate
nonlinear temperature-mortality relationships for eleven cities in the United States from 1973 to 1994. For most of thecities, the relationship is U-shaped and asymmetrical, with asteeper profile in the range of warm temperatures than in therange of cold temperatures. The sensitivity of mortality to
hot and cold temperatures depends on latitude as well as onsocioeconomic and demographic characteristics.7 Though
the specifications include lagged temperatures as control
variables, estimated coefficients for these terms are notreported, making it difficult to compare cumulative effects(net of harvesting) among cities. Basu and Samet (2002)
offer a comprehensive overview of the literature on heatrelated mortality.
The existing evidence on harvesting effects is mixed. In
one of the first studies to allow dynamic effects, Lee (1981)
presents a carefully executed analysis of the impact of
extreme weather on mortality and fertility in England for theperiod 1538 to 1800. Using a distributed lag regression withlags of up to four months, he finds that the summer mortality
effect peaks after a one-month delay, and the winter temperature effect occurs primarily in the current month. In
other words, unusually cold winters are quickly lethal, while
unusually hot summers are slowly lethal, possibly reflecting
the difference between quickly fatal respiratory illnesses
and slowly fatal effects of lower food and water quality due
to hot weather.
These results differ from those of Lars (1990), who uses
U.S. data and finds that the one-month lag effects are
insignificant for summer months, but significantly positivein winter months. Lars documents that a 1 degree Fahrenheit
drop in average monthly temperature does not have asignificant effect on mortality in June or September, but it
increases mortality in the months between October and May
and decreases mortality in July and August.8 In contrast,
Hajat et al. (2002) find that the effect of extreme heat on
mortality is higher in June than in July and August. Since
the other occurrences of extreme heat are primarily in June,
this may be suggestive of a harvesting effect. Hajat et al.
(2002) also document that mortality was strongly affectedby extreme heat during the 1976 heat wave: they calculatethat each degree above 23.3°C is associated with a 6.73%
increase in deaths during this fifteen-day heat wave.9
7 Their estimates of the average effect of a 10-degree Fahrenheit increasein temperature on mortality range from 1.43% in Tampa to 6.56% in
Baltimore.
8 Interestingly, Lars (1990) also finds that the effects differ by state ofresidence in January and February. They are strongest in 1921 in the twosouthern states, which are also the poorest states in the sample. It isdifficult to distinguish whether this is because their residents are lessprepared for cold weather or because temperature variance (which ishigher in the southern states) also increases mortality.
9 Like Curriero et al. (2002), Hajat et al. (2002) use Poisson generalized
additive models (GAMs) to model mortality. This approach allows theinclusion of nonparametric smoothers for seasonality and time trends,

661

Probably the article that is closest to our study is Kunst,
Looman, and Mackenbach (1993). Using data on the Netherlands from 1979 to 1987, the authors find that the lagged
effects of a 1 degree Celsius drop in temperature below
16.5°C remain positive for up to thirty days, yielding acumulative effect of a 1.17% increase in mortality. In
contrast, a 1 degree Celsius increase in temperature above
16.5°C increases deaths only in the next two days anddecreases deaths in subsequent days. More recently, Pattenden, Nikiforov, and Armstrong (2003) estimate a similar
model to investigate differences in the effect of temperature
on mortality in London, England, and Sofia, Bulgaria. Theyfind that 1 degree Celsius of extreme heat (defined as
two-day average of degrees above the 90th centile of twoday mean temperatures, by city) is associated with a 3.49%
immediate increase in mortality in Sofia and a 1.86% increasein London. Defining cold spells by two-week averages, each
additional degree drop (below the 10th centile of two-weekmean temperatures) is associated with a 1.83% increase inmortality in Sofia and a 4.24% increase in London.
Hajat et al. (2005) focus on heat-related mortality andcompare the extent of displacement of mortality in Delhi,São Paulo, and London in the 1991–1994 period. Theirestimates of the immediate effect of each 1 degree Celsius
above the threshold 20°C on daily mortality are 2.2% forDelhi, 1.6% for São Paolo, and 1.4% for London. However,they find that while short-term displacement accounts fornearly all of the immediate effect of excess heat in London,
it is only partially responsible for heat-related mortality inSão Paolo, and even less so in Delhi, where coefficients on
the lag terms remain positive as long as twenty days after
the incidence of excess heat.10
B. Mechanisms

Within certain limits, healthy individuals can cope withthermal stress caused by increases or decreases in ambient
temperatures through thermoregulatory responses. For example, exposure to both high and low temperatures generally triggers an increase in the heart rate in order to increase
blood flow from the body to the skin. Thus, in periods ofprolonged exposure to excessive cold or hot temperatures,
while other explanatory variables are allowed to enter linearly. Whenadjusting for seasonality and other controls, temperature and mortality
tend to have a U-shaped relationship, with a “bliss point” at the temperature that minimizes mortality risk. Some measure of “heat” or “coldness,” typically calculated as degrees away from a specified threshold
temperature, is assumed to have a linear relationship with log mortality.
10 The net excess risk (sum of mortality effects over 28 days) of a 1
degree increase in temperature over 20°C is estimated to be 2.4% in Delhi
and insignificantly different from zero in São Paulo and London. When theestimates are further broken down by age group and cause of death, it isevident that the difference in mortality displacement between Delhi and
London stems from the stark contrast in their at-risk populations: in Delhi,
48% of deaths occur in the age range 0–14, while only 1% of deaths in
London fall in this range. It appears that excess heat affects mortality
primarily through respiratory diseases afflicting persons above age 65 in
London, whereas in Delhi, it also operates through the increased susceptibility of children to infectious diseases.

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the increased cardiovascular stress results in mortality forsome individuals.
The prominent causes of death in periods of elevatedtemperatures are cardiovascular diseases, respiratory diseases, and cerebrovascular diseases. Similarly, cold-related
mortality is also mostly attributable to cardiovascular diseases. The main mechanism underlying the increased mortality in periods of excessive temperature is the additional
stress imposed on the cardiovascular and respiratory systems by the demands of body temperature regulation. These
additional demands can be particularly taxing on individuals
with limited physical ability to adapt, for example, theelderly. The mechanisms linking mortality to cold temperature also stem from an increased burden on the cardiovascular system. Exposure to excessively cold temperature canlead to increased cardiovascular stress because of vasoconstriction and increased blood viscosity. Less is known as towhich population groups are more likely to be affected bysuch effects.
C. Behavioral Risk Factors

The literature has identified several risk factors associatedwith heat-related mortality, though the identification strategies used are sometimes questionable. Most of the risk
factors appear to be related to socioeconomic status. For
example, multiple studies have shown that access to airconditioning greatly reduces mortality risks during period of
elevated temperatures. While socioeconomic factors arestrong predictors of heat-related mortality, other factors also
appear important. Klinenberg (2003) documents the effectof the 1995 Chicago heat wave on mortality. He argues thatthe reason that elderly mortality seems to be more sensitive
to heat waves than the mortality of other age groups isisolation. In addition, persons living in densely population
urban areas have higher risks than those living in rural or
suburban areas because of the phenomenon known as the“urban heat island effect” (Landsberg, 1981). Unfortunately,
there is much less evidence available on the risk factors
associated with cold-related mortality.
D. Indirect Effects

A smaller literature has also established that weather
fluctuations can affect human health through indirect channels. For example, variation in weather generates variationin air pollution. One example of a pollutant that is very
sensitive to weather is ozone, because sunlight and temperature directly affect ozone formation. Weather also affects
health and behavior (such as going outside), so it is potentially correlated with exposure. To the extent that pollution
increases acute episodes of respiratory diseases, it could
affect mortality.11
11 See, for example, U.S. EPA (2003) for a review of the estimatedcorrelations between ozone and health outcomes.

Bhattacharya, Haider, and Currie (2002) examine theeffects of cold weather periods on family budgets and on
nutritional outcomes in poor American families. They findthat poor families increase fuel expenditures and reducefood expenditures in response to cold weather. Weather
events also have important impacts on the incidence of
motor vehicle accidents. Eisenberg and Warner (2005)
found that on snowy days, there were more nonfatal accidents than on dry days, but fewer fatal crashes. They also
found evidence of behavioral adjustment in the sense thatthe first snowy day of the year was associated with substantially higher accident risk than subsequent snowy days.
III.

Data and Preliminary Analysis

The mortality data for this study are drawn from theMultiple Causes of Death (MCOD) files for 1972–1988.12
The key variables for our analysis are the cause and age ofdeath, the exact date of death, and the county of occurrence.13 Our sample consists of all white deaths occurring in
the continental United States. Throughout the analysis, weestimate separate models for males and females and also
estimate the models separately for nine age groups, foreighteen different estimation samples. For each of thesegroups, we construct a balanced panel of mortality totals foreach day between 1972 and 1988. Each of those panels has
18,487,710 observations.14 The balanced MCOD data arethen combined with county-level population totals by age
groups to calculate daily-level mortality rates that we willuse in the analysis.15
The weather data are drawn from the National Climatic
Data Center Summary of the Day Data (TD-3200). The data
are daily measurements from 24,833 weather stations thatwere operational in the United States at some point over the
sample period. The station-level data are aggregated at thecounty level by matching stations to the closest county.
Matches are based on the exact longitude and latitude of theweather station and the longitude and latitude of the county
centroid. For the period 1972–1988, we obtain a panel of
12,534,615 county-day observations with nonmissing information on daily temperature and precipitation.16
12 Since 1968, the MCOD files have provided information on all deaths
occurring in the United States. However, information on exact date ofdeath is available only in the public use data for 1972 to 1988. After 1988,
only the month of death and the day of death are reported in the public use
files.
13 We exclude 130 counties from the analysis because they either
changed name or FIPS over the course of the study period. The majorityof those are from Virginia.
14 There were 6,210 days between 1972 and 1988, so for the 2,977
counties in our MCOD samples, this amounts to 18,487,710 observations.
15 The population counts are from the 1968–1988 Compressed Mortality
Files. They are computed by the Census Bureau, interpolating data fromthe decennial Census of Population, augmented with year-specific information on births, deaths, and migration.
16 In most cases, each county has one or more weather stations. In the
few cases where a county does not have a weather station, we assign thatcounty the closest weather station.

EXTREME WEATHER EVENTS, MORTALITY, AND MIGRATION

663

TABLE 1.—AVERAGE DAILY MORTALITY RATES, BY COUNTYAge Group

Females
All cause mortality
Specific causes of Death
1. Infectious diseases
2. Neoplasms
3. Cardiovasculardiseases
4. Respiratory diseases
5. Motor vehicle
accidents
6. Suicide
7. Diabetes
Males
All cause mortality
Specific causes of death
1. Infectious diseases
2. Neoplasms
3. Cardiovasculardiseases
4. Respiratory diseases
5. Motor vehicle
accidents
6. Suicide
7. Diabetes

All

0

1–9

10–19

20–34

35–44

45–54

55–64

65–74

75⫹

2.2981

1.33700.04750.05010.0847

0.19700.53831.31383.284813.8255

0.02170.49400.03440.00710.00190.00630.00090.00580.00170.01510.00280.07340.00540.24050.01230.53660.02880.97010.11771.8221

1.23720.14050.0287
0.05660.00230.00320.00240.00180.00800.00280.03620.00700.13940.02230.46890.07081.61770.20239.20480.93400.03200.01470.05040.0078—0.00030.00750.00000.00010.01940.00270.00030.01770.00940.00140.01260.01340.00370.01280.01600.01010.01480.01430.03250.02190.0125
0.09300.03240.0116
0.27612.81011.8216

0.06630.12200.22930.36190.94522.30074.838510.4830

0.02850.60430.04750.00760.00230.00860.00100.00860.00520.01720.01080.05860.01120.23310.01920.66080.03751.29120.0882
1.8494

1.36810.21490.03930.08000.00260.00370.00350.00230.01400.00400.09960.00940.41250.03401.11080.13162.5086
0.4032

6.21201.07710.08530.05530.03820.0083—0.00020.01120.00000.00010.05020.01140.00020.06530.03420.0016

0.03450.03120.00520.03040.03400.01240.02910.03670.03330.0311
0.04110.07560.0451
0.04890.1458

Note: The entries are population-weighted average daily mortality rates for the period 1972–1988, by gender, age, and cause of death. The all-age entries are age-adjusted to the gender-specific 1980 population
standard.

Table 1 shows the average daily mortality rates per
100,000 population by age group and gender for selectedcauses of death.17 Unless we note otherwise, all mortality
rates are reported per 100,000 population. Also, all mortality
rates corresponding to the entire age distribution are age
adjusted to the 1980 gender-specific population standard inorder to take into account geographical differences in age
distribution and gender. The table reports that the average
daily mortality rate of females of all ages is 2.30 per
100,000 population. Thus, on average during the 1972–1988
period, for every 100,000 living women, 2.30 died on a
typical day in the United States. The corresponding figures
for males are also reported in table 1. Not surprisingly it islarger, with an all-age daily mortality rate of 2.81 per
100,000.
The typical age profiles of mortality are apparent in an
examination of the columns of table 1. Still, there is also
remarkable heterogeneity in mortality rates across age andgender groups. For all-cause mortality, the female and maleinfant daily mortality rates are 1.34 and 1.80. This issignificant since mortality rates reach this level again only at
the 55–64 age category. The daily mortality rate starts toincrease rapidly at older ages, and in the 75 and above age
category (the last group we consider), it is 13.83 for womenand 10.48 for men.
In addition to all-cause mortality, we consider sevenmortality causes: infectious disease, neoplasms, cardiovascular disease, respiratory disease, motor vehicle accidents,
17 All statistics reported in this paper are weighted by the county
population in relevant year and age group.

suicides, and diabetes. Together these seven causes explain
in excess 85% of the overall mortality rates of males andfemales. As is well known, mortality due to cardiovasculardisease is the single most important cause of death in the
population as a whole. The entries in column 1 suggest thaton a typical day, 1.24 female deaths and 1.37 male deaths
per 100,000 are attributable to cardiovascular disease. However, the relative importance of each cause of death differs
by age. For example, respiratory disease is the most frequent cause of infant death, and motor vehicle accidents are
the most important in explaining mortality up to age 35,
especially for men. Finally, for the population aged 45 andabove, where mortality rates increase rapidly with age,
cardiovascular disease and neoplasms are the two primarycauses of mortality.
A. Seasonal Patterns in Mortality

Figures 1 and 2 illustrate the seasonality of mortality
patterns for each age group. This phenomenon has been well
documented before, though mostly for European countries(see Alderson, 1985; McKee, 1989). Figure 1 shows the fullseasonal patterns of all-cause and cause-specific mortality
rates. For simplicity, we pool males and females and all age
groups, though similar patterns emerge from a genderspecific analysis. On the horizontal axis is each day of theyear, starting at 1 for January 1 and ending at 365 for
December 31 (we excluded February 29 in leap years). Eachline in the figure represents the average mortality rate per
day for all age groups over the period 1972–1988. We
removed the mean of each series in order to have a common

664

THE REVIEW OF ECONOMICS AND STATISTICSFIGURE 1.—AVERAGE DAILY MORTALITY RATES FOR ALL-CAUSE AND CAUSE-SPECIFIC MORTALITY, 1972–1988, PER 100,000 POPULATION (DEVIATIONSFROM DAY-SPECIFIC AVERAGES)

0.4

0.3
0.2
0.1
0.0
-30 0

30 60 90 120 150 180 210 240 270 300 330 360 390-0.1

Daily Mortality Rate Per 100,000

Daily Mortality Rate Per 100,000

0.4

-0.2

0.3
0.2
0.1
0.0
-30 0

-0.2

A. All Mortality Causes

B. Cardiovascular Disease

0.4

0.3
0.2
0.1
0.0
-30 0

30 60 90 120 150 180 210 240 270 300 330 360 390-0.1

Daily Mortality Rate Per 100,000

0.4
Daily Mortality Rate Per 100,000

30 60 90 120 150 180 210 240 270 300 330 360 390

-0.1

0.3
0.2
0.1
0.0
-30 0

30 60 90 120 150 180 210 240 270 300 330 360 390

-0.1
-0.2

-0.2

D. Respiratory Disease

C. Neoplasms

scale for each series. Figure 1A shows the overall mortality
rate. The pervasive seasonality in all-cause mortality isapparent: mortality rates essentially follow a U-shaped pattern, with the peaks in January and December, and thelowest points from mid-July to mid-August. Similarly, cardiovascular mortality, displayed in figure 1B also follows
U-shaped pattern. However, the season trend of all-cause
mortality is not mirrored in all the specific causes. For
example, there is essentially no seasonality in mortality due
to neoplasms, as seen in figure 1C. Finally, figure 1D showsthat respiratory disease mortality is also concentrated in thewinter months.
Seasonal patterns are not the same everywhere. Figure 2
documents the geographical variation in the seasonal patterns of mortality. To this end we compare Suffolk County,
Massachusetts (which includes the city of Boston), and San
Diego County, California (which includes the city of San
Diego). These counties were chosen because of the marked
difference in their winter climate and because of the similarity of their summer climate and other characteristics, suchas per capita income.
Again, we removed the mean of each series in order tohave a common scale for each figure. In order to emphasize the main trends, the series were smoothed using aseven-day moving average. Figure 2A shows the average
daily all-cause mortality rates of all age groups for

Suffolk (solid line) and San Diego (dashed line). For bothcounties, we observe that mortality rates follow theU-shaped seasonal patterns shown in figure 1, but alsowith geographical differences. For example, it is apparent
that the mortality rate is higher in Suffolk than in San
Diego in the winter months (days 1–90). Figures 2B to
2D further document the seasonal differences in mortality
rates between San Diego and Suffolk by examining
mortality rates for specific causes of death. Cardiovascular mortality and, to a lesser extent, respiratory diseases
show excess mortality rates in Suffolk during the winterdays. Neoplasms show essentially no seasonal patterns
for both counties, as was the case in figure 1. There isalso little evidence of significant difference of excess
winter mortality due to diabetes and external causes (notshown).

IV.

Estimates of the Effect of Extreme Temperatures on
Mortality

In this section we present static estimates of the effect oftemperature shocks on mortality. We begin in section IVA
by presenting estimates of the contemporaneous effect ofheat and cold waves on mortality. In section IVB, weconsider a more general model that includes the effect of

EXTREME WEATHER EVENTS, MORTALITY, AND MIGRATION

665

0.6
0.4
0.2
0
-30

0

30 60 90 120 150 180 210 240 270 300 330 360 390-0.2

Daily Mortality Rate Per 100,000

Daily Mortality Rate Per 100,000

FIGURE 2.—AVERAGE DAILY MORTALITY RATES FOR SUFFOLK COUNTY, MA, AND SAN DIEGO COUNTY, CA, 1972–1988, PER 100,000 POPULATION(DEVIATIONS FROM COUNTY ⫻ DAY-SPECIFIC AVERAGES).

-0.4

0.6
0.4
0.2
0
-30

0

-0.4
A. All Mortality Causes

B. Cardiovascular Disease

Suffolk County, MA

San Diego County, CA

0.6
0.4
0.2
0
-30

0

30 60 90 120 150 180 210 240 270 300 330 360 390

-0.2
-0.4

Daily Mortality Rate Per 100,000

San Diego County, CA

Daily Mortality Rate Per 100,000

30 60 90 120 150 180 210 240 270 300 330 360 390-0.2

Suffolk County, MA

0.6
0.4
0.2
0
-30

0

30 60 90 120 150 180 210 240 270 300 330 360 390

-0.2
-0.4

C. Neoplasms
San Diego County, CA

D. Respiratory Disease
Suffolk County, MA

San Diego County, CA

heat and cold waves on mortality not only on the days of theextreme weather event, but also the days and weeks following. This model allows us to calculate the long-run effect of
the event, net of any harvesting and accounting for any
delayed impacts in the effect. In section IVC, we differentiate the effect by cause of death. Finally, in section IVD, weinvestigate alternative specifications and extensions, in particular whether the effect depends on county income andrelative exposure.
A. Contemporaneous Effect

To quantify the contemporaneous effect of extreme temperature on mortality in any given day and location, we
estimate a simple linear model relating the daily mortality
rate in a county, Y cdt , to a daily temperature measurement
for this county (T cdt ):18
Y gcdt ⫽ ␣ g ⫹ ␤ g T gcdt ⫹ ␭ gcmt ⫹ u gcdt ,(1)

18 Since the literature is unclear as to whether mortality is more related
to daytime or nighttime temperatures, we use the 24-hour average temperature for each day.

Suffolk County, MA

where g denotes gender, c denotes county, d denotes day of
the year (1–365; for simplicity we eliminated February 29 inleap years), m (1–12) denotes month, and t denotes year
(1972–1988). In order to account for seasonality and geographical differences in mortality patterns documented in
the previous section, we include a series of county-by-yearby-month effects, ␭ gcmt . With 17 years of data and 2,279
counties, there are approximately 400,000 such effects.
These effects are allowed to vary by gender and will beallowed to vary by gender and age in the age-specificmodels reported here. We also include a quadratic in daily
precipitation, although it is of little importance in explaining
mortality in practice. Finally, since weather and mortality
are likely to be serially correlated over time within the
county, all standard errors reported in this paper are clustered at the county level.
Under the assumption of a linear additive model, thegender-by-county-by-year-by-month effects nonparametrically account for all the determinants of mortality that varyacross gender, counties, and months over time, as well as forthe monthly-level seasonality in mortality. So, for example,
permanent differences in health care services or the overall

666

THE REVIEW OF ECONOMICS AND STATISTICS
TABLE 2.—CONTEMPORANEOUS ESTIMATES OF THE EFFECT OF COLD AND HOT TEMPERATURE ON DAILY ALL-CAUSE MORTALITY RATES
Females

Mean daily temperature
Fraction of cold/hot days
All cause mortality (s.e.)
Percent effect
Cause-specific
1. Infectious diseases
2. Neoplasms
3. Cardiovasculardiseases
4. Respiratory diseases
5. Motor vehicle
accidents
6. Suicide
7. Diabetes

Males

⬍20
0.042
0.0033(0.0050)
0.1

⬍30
0.105
⫺0.0028(0.0037)
⫺0.1

⬎80
0.070
0.1076(0.0097)
4.7

⬎90
0.006
0.0678(0.0151)
3.0

⬍20
0.042
0.0396(0.0058)
1.4

⬍30
0.105
0.0379(0.0046)
1.3

⬎80
0.070
0.1114(0.0109)
4.0

⬎90
0.006
0.0564(0.0141)2.00.0014(0.0005)
0.0145(0.0025)
0.0374(0.0040)0.0070(0.0014)0.0005(0.0005)
0.0002(0.0004)
0.0006(0.0009)

0.0001(0.0004)
0.0127(0.0018)
0.0384(0.0032)
0.0068(0.0010)⫺0.0003(0.0004)⫺0.0006(0.0003)
0.0042(0.0027)0.0011(0.0015)⫺0.0014(0.0019)0.0124(0.0061)
0.0034(0.0014)0.0003(0.0006)
0.0002(0.0004)⫺0.0012(0.0008)⫺0.0013(0.0015)
0.0030(0.0057)
0.0246(0.0102)0.0002(0.0028)0.0006(0.0017)0.0018(0.0010)⫺0.0015(0.0020)0.0002(0.0005)
0.0140(0.0024)
0.0565(0.0043)0.0099(0.0018)0.0006(0.0009)
0.0002(0.0007)0.0008(0.0007)0.0006(0.0004)
0.0130(0.0019)0.0458(0.0030)
0.0094(0.0014)0.0011(0.0007)0.0009(0.0005)
0.0007(0.0005)

0.0004(0.0005)⫺0.0027(0.0024)0.0154(0.0054)0.0004(0.0015)0.0016(0.0010)⫺0.0006(0.0007)0.0002(0.0006)⫺0.0034(0.0011)⫺0.0009(0.0093)0.0074(0.0119)0.0500(0.0049)
0.0054(0.0028)⫺0.0024(0.0024)0.0005(0.0015)

Note: Standard errors clustered by county are reported in parentheses. The first row shows the fraction of days in the sample where the mean temperature falls below or above the specified daily mean temperature.
Entries in all the other rows are estimates of the coefficient on whether mean daily temperature is above or below the prespecified level (the coefficient ␤ in equation [1]). Each entry is from a separate regression.
The dependent variable is the daily mortality rate, age-adjusted to the gender-specific 1980 population standard. All models include a series of county-by-year-by-month effects. Percent effect is the ratio of theestimated effect and the mean daily mortality rate reported in table 1.

health attributes of the local gender- and age-specific populations will not confound the temperature variables. This isalso important since seasonality in mortality has been
known to confound estimates of the temperature-mortality
relationship (Mackenberg, Kunst, & Looman, 1992). As
such, the temperature effect on mortality is identified fromcounty-by-year-by-month deviations in temperature. Another clear advantage of using random shocks in temperature to identify the models is that it mitigates the possibilityof measurement error bias. The number of deaths in small
and medium-sized counties on a given day is likely to berather noisily measured. Fortunately, in that case, the measurement error in the dependent variable will be uncorrelated with the temperature variables on the right-hand side
once we condition on the county-by-month-by-year fixed
effects. Since the daily mortality in low-population counties
may exhibit sizable day-to-day variation, we also weight all
regression models by county population.
We experimented with several possible specifications of
the temperature effects. We begin in table 2 by reporting theestimates where T cdt is a dummy variable equal to 1 if themean daily temperature in county c, day d, and year t is
below or above a predetermined threshold. Mean temperature on a given day is defined as the simple average of theminimum and maximum temperature that day. Since theunderlying model relating weather and mortality is unknown, we examine several possible thresholds, corresponding to cold- and heat-related mortality.19 For both
19 Other aspects of daily weather such as humidity and wind speed could
influence mortality, both individually and in conjunction with temperature.
Importantly for our purposes, there is little evidence that wind chill factors(a nonlinear combination of temperature and wind speed) perform better
than simple temperature levels in explaining daily mortality rates (Kunst,
Groenhof, & Mackenbach, 1994).

females and males, we consider two thresholds for cold
temperature exposures (daily mean temperatures less than
20°F and 30°F, respectively) and two thresholds for hot
temperature exposures (daily mean temperatures exceeding
80°F and 90°F, respectively).
The first row of table 2 shows the fraction of days in our
sample where the population is exposed to cold and hot
days, weighted by the relevant population. For example,
4.2% of all days have a mean temperature below 20°F, and
0.6% of all days have a mean temperature above 90°F.
The estimates for the cold temperature models indicate asmall immediate increase in mortality on cold days formales but no such relationship for females. For example, theall-cause male mortality rate increases by 0.0396 on days
when the mean temperature falls below 20°F. This impact
corresponds to a 1.4% effect, compared to the mean daily
mortality rate reported in table 1. For females, the corresponding impacts are small (a 0.1% impact) and statistically
imprecise. The remaining rows are organized by mortality
cause. Examination of the cause-specific estimates reveals
three significant findings. First and foremost, the estimatedcold temperature mortality effect is to a very large extent
driven by excess cardiovascular mortality on cold days.
Second, there is also clear evidence that cold days areassociated with increased mortality from neoplasms. Finally, the other cause of death significantly accelerated bycold temperatures is respiratory diseases. In all causes
considered in table 2, there is little evidence of differences across gender in the magnitude of the contemporaneous cold mortality impacts. Importantly, all the cold
mortality estimates reported in table 2 have similar magnitudes regardless of the chosen threshold for cold temperatures.

EXTREME WEATHER EVENTS, MORTALITY, AND MIGRATION

Unlike the moderate impacts of cold temperature days on
all-cause daily mortality rates, the estimates for hot temperature are much larger in magnitude. For males and females,
the all-cause mortality rate increases by 0.10 to 0.11 on days
when the mean temperature goes above 80°F, corresponding
to a 4% effect. Similarly, mortality rates are higher on days
when the average temperature goes above 90°F, although
the magnitude of the impact is smaller (2%–3% effects).
Turning to specific causes of death, the entries in table 2
suggest that excess mortality immediately following exposure to high temperatures is mostly attributable to cardiovascular diseases (CVD). The immediate impact of heat on
cardiovascular diseases mortality has been reported elsewhere (see Braga et al., 2002; Huynen et al., 2001). Interestingly, the contemporaneous effect of high temperatures
on CVD is smaller than the contemporaneous effect of cold
exposure on CVD.
In conclusion, the evidence in table 2 suggests thatmortality rates are significantly higher on both cold and hot
days, but that the excess mortality on hot days is substantially larger (at least three times larger) than on cold days.
This evidence is consistent with the popular notion that heatwaves (and, to a lesser extent, cold waves) significantly
increase mortality, and with the dramatic characterization ofthese events found in the popular press.
B. Dynamic Effect

The results reported so far do not take into account thepotentially dynamic relationship between temperature exposure and mortality. It is possible that deaths resulting fromextreme temperature could constitute near-term mortality
displacement. In other words, extreme temperatures may
simply anticipate the death of individuals whose health isalready compromised and who would have died a few days
later even in the absence of the event. In this case, the only
effect of the temperature shock is to change the timing ofmortality by a few days, not the number of deaths over alonger period. Such temporal displacement is sometimes
referred to as the harvesting effect. If this is the case,extreme temperatures could have no significant permanent
effect on life expectancy, and the contemporaneous estimates reported in table 2 could grossly overstate the mortality effect of cold and hot temperature shocks.
It is also possible that the presence of dynamic effects
may have the opposite effect. This could happen, for example, if an unusually low temperature today results in increased mortality over the next few days or weeks, becausesome respiratory conditions take some time to fully develop
and spread. This delayed response would imply that thecontemporaneous estimates in table 2 underestimate the truelong-run effect.
Ultimately whether the long-run effect is larger or smaller
than the short-run effect is an empirical question. We investigate this possibility by including a distributed lag structure
in our models:

冘␤ T

667

J

Y gcdt ⫽ ␣ g ⫹gj

gcdt⫺j⫹ ␭ gcmt ⫹ u gcdt .

(2)

j⫽0

This model allows the effect of temperature up to J daysin the past to affect mortality rates today. In equation (2), thetotal effect of temperature on mortality rates for a given
gender group (also called dynamic causal effect) is obtainedby summing the coefficients on the contemporaneous andJ
lagged temperature variables, ¥ j⫽0
␤ˆ gj . 20 The dynamic
causal effect measures the combined effect of temperature
today, yesterday, and so forth on mortality rates today.
Different lag structures potentially generate different estimates of the dynamic causal effect. In our context, therelationship between the dynamic causal effect and the lag
length is informative about the extent of mortality displacements attributable to temperature shocks. If temperatureshocks lead to temporal displacement of mortality (forexample, harvesting), then there should be a negative relationship between the estimated dynamic causal effect andthe lag length. In other words, if there is harvesting, then theimmediate increase in mortality in the first few days following a hot or cold shock (implying a positive dynamic
causal effect for short lag lengths) should be followed witha corresponding compensatory effect where mortality in the
weeks following the shock declines relative to the trend(implying a negative dynamic causal effect for medium tolong lag lengths).
The richness of our data and our large sample sizes allow
us to control the independent effect of temperature in eachof the thirty days preceding a given recorded death. We
choose thirty days for our base specification because it
appears unlikely that temperature shocks have significant
lagged effects after one month. Later, we estimate models
with lags of sixty and ninety days and find that, consistent
with this assumption, the quantitative results do not change
significantly.
Figures 3 and 4 display the estimates on current andlagged temperatures as well as their standard errors as afunction of the displacement. The left panel of figure 3
shows the dynamic response function associated with cold
temperature exposure (days when the mean temperature is
below 30°F) for females, and the right panel shows the samefor hot temperature exposure (days when the mean temperature is in excess of 80°F). Figure 4 is organized similarly
for males. The main findings of the paper are apparent: inthe case of exposure to high temperature, there is an immediate and large increase in mortality. For males and females
the, magnitude of this excess mortality ranges from 0.08 to
0.10 daily deaths per 100,000. However, within three daysof the shock, the effect has completely dissipated, and the
20 This dynamic causal effect is sometimes referred to as the cumulative
dynamic multiplier. See Stock and Watson (2003) for an insightful
discussion of dynamic causal effects. Consistent estimation requires thatE[u gcdt 兩␭ gcdt , T gcdt , T gcdt⫺1 , . . . , T gcdt⫺J ] ⫽ 0.

668

THE REVIEW OF ECONOMICS AND STATISTICSFIGURE 3.—ESTIMATED COEFFICIENTS FROM DYNAMIC MODELS OF THE EFFECT OF COLD AND HOT TEMPERATURE EXPOSURE ON DAILY FEMALE
ALL-CAUSE MORTALITY RATES(B) Daily Mean Temperature More than 80°F

0.10
0.08
0.06
0.04
0.02
0.00
-0.02

0

5

10

15

20

25

30

Daily Mortality Rate Per 100,000

Daily Mortality Rate Per 100,000(A) Daily Mean Temperature Less than 30°F

0.10
0.08
0.06
0.04
0.02
0.00
-0.02

0

5

10

15

20

25

30

-0.04

-0.04

Displacement (in days)

Displacement (in days)-1 std err

daily temperature impact+1 std err-1 std err

estimated effects hover around the 0 line. A notably different pattern emerges with cold temperatures. In this case, theimmediate mortality response to the shock is smaller andpeaks two to three days following the shock. What isremarkable is the magnitude and significance of the dynamic response at larger lags. For males and females, cold
temperature exposure still has a significant effect on mortality rates ten to fifteen days following the exposure.
Table 3 examines these dynamics with more detail. Eachrow reports the independent effect of lagged temperature
variables, estimated in a model where thirty lags are included. The coefficients in the first row (the 0 lag independent effect) measure the contemporaneous effect of today’s
temperature on today’s mortality, conditional on the temperature for the past thirty days. The coefficients in thesecond row (the lag “1–2” independent effect) measure the
combined effect of the temperature in the two preceding
days on today’s mortality, conditional on today’s temperature and the other lags. In terms of equation (2), thisˆ g2 . The interpretation of the coefˆ g1 ⫹ ␤corresponds to ␤
ficients in the other rows is similar. Finally, the thirty-day

daily temperature impact+1 std err

dynamic causal effect in the last row is the sum of thecoefficients on the contemporaneous temperature dummy
variables and the coefficients on all lagged temperature
30 ˆ␤ gj . This measures the long-term
dummy variables: ¥ j⫽0
effect of the temperature shock.
It is clear from the results in table 3 that the contemporary
effect of temperature is vastly different for hot and cold
days. The estimates for hot temperature indicate that on hot
days, mortality immediate increases, as was shown in table
2. For example, on days when the average temperature
raises above 80°F, the death rate increases by 0.083 points
for females and 0.098 points for males. Both effects are
precisely estimated with standard errors in the 0.007–0.009
range. However, there is no such immediate relationship forcold days: the estimates for the cold temperature thresholds
are either negative or statistically insignificant.21 The effectof lags 1 and 2 measures the cumulative effect of one day of
21 The negative cold effect for contemporaneous temperature in such
dynamic models has been found elsewhere as well (see Huynen et al.,2001), but the epidemiology literature has yet to explain it.

FIGURE 4.—ESTIMATED COEFFICIENTS FROM DYNAMIC MODELS OF THE EFFECT OF COLD AND HOT TEMPERATURE EXPOSURE ON DAILY MALE ALL-CAUSEMORTALITY RATES(B) Daily Mean Temperature More than 80°F

0.10
0.08
0.06
0.04
0.02
0.00
-0.02

0

5

10

15

20

25

30

Daily Mortality Rate Per 100,000

Daily Mortality Rate Per 100,000(A) Daily Mean Temperature Less than 30°F

0.10
0.08
0.06
0.04
0.02
0.00
-0.02

0

5

10

15

20

25

-0.04

-0.04

Displacement (in days)

Displacement (in days)-1 std err

daily temperature impact+1 std err-1 std err

daily temperature impact+1 std err

30

EXTREME WEATHER EVENTS, MORTALITY, AND MIGRATION

669

TABLE 3.—CUMULATIVE DYNAMIC ESTIMATES OF THE EFFECT OF COLD AND HOT TEMPERATURE ON DAILY ALL-CAUSE MORTALITY RATES
Females
Mean daily temperature
Fraction of cold/hot days
Independent effect of lags
0
1–2
3–6
7–14
15–30
30-day cumulative effect30-day cumulative effect(controlling forcounty⫻month andstate⫻year effects)

Males

⬍20
0.042

⬍30
0.105

⬎80
0.070

⬎90
0.006

⬍20
0.042

⬍30
0.105

⬎80
0.070

⬎90
0.006

⫺0.0225(0.0057)⫺1.0
0.0768(0.0079)
3.3
0.0956(0.0089)
4.2
0.0591(0.0108)
2.6
0.0236(0.0206)
1.0
0.2326(0.0206)
0.1159(0.0194)⫺0.0316(0.0042)⫺1.4
0.0741(0.0053)3.2
0.0806(0.0068)
3.5
0.0764(0.0082)
3.3
0.0572(0.0107)2.5
0.2567(0.0150)
0.1680(0.0154)0.0830(0.0067)
3.6
0.0636(0.0120)2.8⫺0.0074(0.0093)⫺0.3⫺0.0407(0.0096)⫺1.8⫺0.1032(0.0144)⫺4.5⫺0.0046(0.0220)0.0407(0.0286)

0.0412(0.0172)1.8
0.0672(0.0309)2.9
0.0032(0.0238)0.1⫺0.0213(0.0273)⫺0.9⫺0.0957(0.0363)⫺4.2⫺0.0054(0.0640)0.0644(0.0590)0.0089(0.0068)0.3
0.0851(0.0084)
3.0
0.0905(0.0093)
3.2
0.0268(0.0123)
1.0⫺0.0337(0.0183)⫺1.2
0.1797(0.0243)
0.1379(0.0219)0.0097(0.0050)0.3
0.0726(0.0059)
2.6
0.0890(0.0074)
3.2
0.0313(0.0089)
1.1⫺0.0027(0.0116)⫺0.1
0.1998(0.0168)
0.1706(0.0192)

0.0981(0.0089)
3.5
0.0319(0.0112)1.1⫺0.0194(0.0086)⫺0.7⫺0.0521(0.0124)⫺1.9⫺0.0935(0.0161)⫺3.3⫺0.0349(0.0251)
0.0565(0.0269)

0.0506(0.0170)1.8
0.0497(0.0366)
1.8
0.0285(0.0365)
1.0⫺0.0219(0.0355)⫺0.8⫺0.1369(0.0388)⫺4.9⫺0.0300(0.0529)0.1027(0.0637)

Notes: Standard errors clustered by county are reported in parentheses. Each column is from a separate regression. The dependent variable is daily mortality rate, age-adjusted to the gender-specific 1980 population
standard. All models include a series of county-by-year-by-month effects (except in the last row, where the models control for county-by-month and state-by-year effects). The first row shows the fraction of daysin the sample where the mean temperature falls below or above a given threshold. Entries in all the other rows are the effects of lagged temperature dummy variables, estimated in a model where thirty lags areincluded. For example, the coefficients in the second row (the 0 lag independent effect) measure the contemporaneous effect of today’s temperature on today’s mortality, conditional on the temperature for the past
thirty days. The coefficients in the third row (the lag “1–2” independent effect) measure the combined effect of the temperature in the two preceding days on today’s mortality, conditional on today’s temperatureand on the other lags (this is ˆ␤ gl ⫹ ˆ␤ g2 in equation [2]). The thirty-day dynamic causal effect in the last row is the sum of the coefficients on the contemporaneous temperature dummy variable and the coefficients30 ˆ␤ gj .
on all lagged temperature dummy variables: ¥ j⫽0

cold or hot temperature in the last two days affects themortality rate today. Again, there is a remarkable difference
between cold and heat effects: The one- to two-day lag
effects for heat are attenuated compared to the contemporaneous, while the cold estimates are remarkably larger thanthe contemporaneous ones. For example, exposure to one
day with temperatures above 80°F in the past two days has
a cumulative effect on the male daily mortality rate of 0.032
points, while exposure to temperature below 30°F in thepast two days raises the male daily mortality by 0.073
points. Notably, the difference between the days 1 and 2
cumulative effect of cold and hot temperature exposure ismuch smaller for females, though the cold temperatureeffect still dominates (0.074 deaths per 100,000 versus
0.064 deaths per 100,000). This discrepancy in the dynamic
response between males and females already points to oneof our main findings: the adjustment window for female
mortality following a cold temperature shock is longer thanthat of males.
At longer displacements, the divergence between the hot
and cold temperature effects on mortality is even more
apparent. Perhaps this is best exemplified by the impact atthree to six days of displacement: The effect of exposure tocold temperatures on mortality ranges between 0.08 and0.10 deaths per 100,000, while the effect of exposure to hottemperatures is small in magnitude and statistically insignificant. It is worth noting that the effect of days with mean
temperature below 30°F on female mortality remains positive and significant at all displacements considered.

However, for heat-related mortality, the effect of temperature at longer displacements is negative and generally
statistically significant, with little discernable differences
between males and females. Thus, the initial increase inmortality following a hot day is compensated for with adecline in mortality in the subsequent days, consistent withthe harvesting hypothesis. This result applies to both hottemperature thresholds considered and to both males andfemales.
Finally, we report two different estimates of the thirty-day
cumulative effects in the last row. The first, labeled “30-day
cumulative effect,” is simply the sum of the coefficients ofthe different displacements reported in the rows above. It isbased on the baseline model that includes gender-specificcounty-by-year-by-month fixed effects.
The results are striking. The cumulative effect of one cold
temperature day raises the daily mortality rate by 0.18 to
0.29 points, corresponding to percentage effects of 6.4% to11.2%. For example, the thirty-day cumulative effect of one
day of temperature below 30°F for females leads to anincrease in daily mortality rates by 0.2567 points, whichcorresponds to a 8.9% effect. Across gender and specification, the estimates of cold-related mortality are precise, witht-statistics ranging from 7.4 to 17.1. However, no significant
effect is discernible for extreme hot temperature above 80°F
or 90°F. Extreme heat shocks seem to precipitate the healthcondition of individuals who are already weak and would
have died even in the absence of the shock. The only effectof a heat shock is a minor change in the timing of mortality.

670

THE REVIEW OF ECONOMICS AND STATISTICSFIGURE 5.—ESTIMATED RELATIONSHIP BETWEEN DAILY TEMPERATURE AND MORTALITY RATES

Daily Mortality Rate Per 100,000

0.60
0.50
0.40
0.30
0.20
0.10
0.00
-0.10

<10

10-20

20-30

30-40

40-50

50-60

60-70

70-80

80-90

>90

Daily Temperature
Contemporaneous EffectCumulative Dynamic EffectNotes: Dashed lines represent ⫹/⫺ 1 standard error. The models reported here pool males and females and are based on a 50% random sample of the overall sample. The dependent variable is daily mortality
rate, age-adjusted to the 1980 population standard. All models include a series of county-by-year-by-month effects. Contemporaneous effects are estimates of the coefficient on whether mean daily temperature isin each of the ten daily temperature categories, relative to the 60°F–70°F category. Cumulative dynamic effects are estimates from models with thirty lags in daily temperature in each of the ten categories (again
relative to the 60°F–70°F category). The reported effect here is the sum of the coefficients on the thirty lags in each category.

The second, labeled “30-day cumulative effect, controlling for county ⫻ month and state ⫻ year effects,” is a more
restrictive version of the model, whereby the unrestrictedeffects are now defined by county-by-month and state-byyear. Like the baseline specification, this model allows
county-specific seasonality in mortality patterns (as suggested by figure 2) and allows controlling for secular trends
in mortality that evolve relatively slowly (e.g., at the year
level). The advantage of this specification is that the point
estimates are identified relative to the historic normals for acounty and month rather than relative to a county and monthin the current year. Its main disadvantage is that it requires
computational power beyond the capacity of most servers.
As such, the estimates reported here are for a 50% sample
of our baseline sample. All in all, the estimates from theother model are qualitatively similar to those of the baselinemodel. The cumulative effects of exposure to cold temperature on mortality are positive and significant, althoughsmaller in magnitude. The cumulative effects of exposure to
hot temperature are small and positive, but statistically
imprecise and insignificant.
As pointed out above, our definition of cold and heatwave is somewhat arbitrary. While in table 3 we show the
cumulative effect for different definitions of heat and cold
wave, in figure 5 we show estimates from models where theindependent variable is the dummy variable for days inthe temperature range 0–10, 10–20, 20–30, and so on.22 As
the figure makes clear, excess mortality occurs at the extremes of the temperature distribution. Moreover, the statistical adjustments for dynamic displacements (harvestingand delayed impacts) are apparent. Again, the contempora22 For computational ease, we have pooled males and females and use a
50% random sample of our main sample. This model has 400,000 fixed
effects and 270 regressors.

neous model understates the effect of cold exposure andoverstates the effect of heat exposure on mortality. Importantly, the relationship is monotonic: predicted mortality
rates are highest at the two extremes of the temperature
distribution.
Overall, the evidence in table 3 and figure 5 points to an
important conclusion of this paper: increases in heat-related
mortality observed during heat waves appear to be mostly
an artifact of harvesting and completely disappear withinweeks. In other words, the immediate effect of heat on
mortality is mostly driven by temporal displacement. By
contrast, there is no evidence of harvesting associated withcold-related mortality. The immediate increase in mortality
caused by extreme cold weather is not followed by areduction in the following weeks. As a consequence, it is along-lasting effect that has the potential of inducing significant changes in a person’s longevity. In sections V and VI,
we quantify the effect on longevity.
C. Dynamic Estimates by Age and Cause of Death

We now turn to estimates of the effect of cold temperature
on mortality by age group and cause of death. This exercise
provides valuable information about the pathways between
cold temperature and mortality. Each column in tables 4(females) and 5 (males) corresponds to an age group, andeach row corresponds to a specific cause of death. We report
the thirty-day total effect corresponding to days with temperature below 30°F.
First, we describe the all-age estimates reported in column 1. These results are remarkable: for both males andfemales, the leading cause of cold-related excess mortality
is cardiovascular disease. The results indicate that respiratory disease is also important, accelerated by exposure tocold temperatures. Together, these two causes alone explain

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671

TABLE 4.—CUMULATIVE DYNAMIC ESTIMATES OF THE EFFECT OF COLD TEMPERATURE ON DAILY MORTALITY RATES: FEMALES, BY AGE AND CAUSE OF DEATH

All cause mortality:
Mean (daily)
30-day cumulative effect:(s.e.)
Percent effect
Cause-specific mortality
1. Infectious
2. Neoplasms
3. Cardiovascular
4. Respiratory
5. Motor vehicle
accidents
6. Suicide
7. Diabetes

(1)
All

(2)
Below 1
year

(3)
Ages
1–9

(4)
Ages
10–19

(5)
Ages
20–34

(6)
Ages
35–44

(7)
Ages
45–54

(8)
Ages
55–64

(9)
Ages
65–74

(10)
Ages
75⫹

2.29810.2567(0.0150)
11.2

1.33700.0271(0.0600)
2.0

0.04750.0066(0.0039)13.9

0.05010.0006(0.0034)1.2

0.0847⫺0.0004(0.0032)⫺0.5

0.19700.0076(0.0076)
3.9

0.53830.0289(0.0150)
5.4

1.31380.0695(0.0248)
5.3

3.28480.1839(0.0462)
5.6

13.8255
2.3030(0.1214)16.7

0.0050(0.0013)0.0010(0.0064)
0.1654(0.0113)0.0478(0.0038)⫺0.0021(0.0015)⫺0.0031(0.0011)0.0111(0.0047)⫺0.0072(0.0090)⫺0.0001(0.0048)0.0093(0.0104)
0.0105(0.0127)
0.0030(0.0038)——0.0002(0.0012)0.0013(0.0008)
0.0005(0.0013)0.0016(0.0009)⫺0.0005(0.0010)0.0000(0.0015)0.0000(0.0000)
0.0000(0.0002)⫺0.0005(0.0004)⫺0.0021(0.0011)0.0010(0.0008)
0.0008(0.0007)⫺0.0033(0.0019)0.0001(0.0007)⫺0.0001(0.0003)⫺0.0002(0.0005)
0.0019(0.0014)⫺0.0014(0.0012)⫺0.0001(0.0006)⫺0.0011(0.0015)⫺0.0013(0.0012)0.0001(0.0005)⫺0.0002(0.0008)0.0047(0.0049)
0.0032(0.0034)
0.0042(0.0014)0.0001(0.0019)⫺0.0025(0.0021)0.0039(0.0012)⫺0.0009(0.0013)0.0053(0.0094)
0.0187(0.0075)
0.0046(0.0028)0.0015(0.0022)⫺0.0019(0.0022)0.0064(0.0021)0.0025(0.0023)⫺0.0006(0.0148)0.0214(0.0138)
0.0135(0.0056)0.0008(0.0021)⫺0.0006(0.0021)0.0027(0.0039)0.0095(0.0034)⫺0.0181(0.0221)0.1049(0.0331)0.0495(0.0113)⫺0.0043(0.0032)⫺0.0043(0.0022)0.0034(0.0071)0.0343(0.0096)0.0444(0.0391)
1.5474(0.0950)0.4000(0.0323)⫺0.0090(0.0044)⫺0.0059(0.0025)
0.0447(0.0156)

Notes: Standard errors clustered county in are reported in parentheses. Entries are estimates of the cumulative effect of cold temperature on mortality over thirty days. Each column reports estimates from theage-specific model listed in the column heading. For the all-age model, the dependent variable is the all-cause or cause-specific mortality rate, age-adjusted to the 1980 gender-specific population standard. Eachrow corresponds to a specific cause of death. We report the thirty-day cumulative effect corresponding to days with temperature below 30°F.

83% and 94% of the overall mortality impact for females
and males, respectively. There are also interesting genderdifferences. For example, female deaths due to diabetes are
significantly increased by exposure to cold temperatures,
while male deaths due to motor vehicle accidents are significantly reduced following a cold temperature shock.23
Column 2 shows estimates for infant deaths (less than 1
year old). The dynamic causal effects for all-cause mortality
are positive for females and males (0.0271 and 0.0923), but
imprecisely estimated. In fact, none of the cause-specificmortality rates of infants are significantly changed by cold
temperatures.
An interesting finding in table 5 is that for male teenagers
and male young adults (the 10–19 and 20–34 age categories),
the dynamic causal effects for all-cause mortality are negativeand statistically significant. For example, in column 5, thedynamic causal effect reported is ⫺0.0254, corresponding toan 11.1% reduction in daily mortality rates for that age group.
This impact is mostly attributable to a causal effect between
cold temperature and lower rates of motor vehicle accident
mortality. One possible explanation for this finding is thatsnowfall is more likely on colder days and that snowfall has
been shown to be associated with fewer fatal car accidents(Eisenberg & Warner, 2005). It is also notable that such effects
are not detected for females in table 4.
For prime-aged adults (45 and above), the evidence ofexcess mortality as a result of cold days is definitive. The
estimates of the cumulative effect of one cold day on daily
mortality rates are positive and precisely estimated. The

magnitude of the excess mortality caused by cold temperature increases with age for both genders. For females, it
increases from 0.0289 per 100,000 for the 45–54 age group,
to 2.3030 per 100,000 for the age group 75 years and over.
For males, the mortality impact also increases dramaticallyafter the age of 45, from 0.0490 to 1.2721 per 100,000.
Since mortality rates also increase with age, this result
may be misleading. However, similar patterns are observed
when the estimates as percentage effects relative to theage-specific average mortality rates are reported. The associated percentage effects increase from 5.4% to 16.7% forfemales and from 5.2% to 12.1% for males. To the best of
our knowledge, we are the first to document this finding fornarrowly defined age groups.24 Examination of the causespecific estimates reveals that excess CVD mortality is themain driver of the age-increasing mortality impacts. Excess
respiratory disease is also an important explanation for theage patterns. There is also no evidence of a connection
between neoplasms and cold temperature for both genders.
Taken as a whole, the results in tables 4 and 5 indicatethat the cold temperature effect is stronger for older age
groups and is mostly concentrated in excess cardiovascularmortality. The estimated impacts are not attributable totemporary displacement of deaths and thus represent a
potentially significant reduction in longevity. However, wenote that one important limitation of our analysis of mortality by cause of death is that each cause of death represents
a competing risk. A change in the incidence of one cause ofdeath therefore changes the pool of individuals at risk to die

23 The number of suicides and deaths for diabetes is also associated withcold weather. We speculate that extreme bad weather may reduce suicides
by reducing the likelihood that people leave their house. We do not havea good explanation for the positive coefficient on diabetes.

24 There is some evidence in the previous literature that the elderly are
more sensitive to temperature fluctuations. However, it is not always easy
to interpret these estimates because they are based on less transparent
research designs and much broader age categories.

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THE REVIEW OF ECONOMICS AND STATISTICS

TABLE 5.—CUMULATIVE DYNAMIC ESTIMATES OF THE EFFECT OF COLD TEMPERATURE ON DAILY MORTALITY RATES: MALES, BY AGE AND CAUSE OF DEATH

All cause mortality:
Mean (daily)
30-day cumulative effect(s.e.)
Percent effect
Cause-specific mortality:
1. Infectious
2. Neoplasms
3. Cardiovascular
4. Respiratory
5. Motor vehicle
accidents
6. Suicide
7. Diabetes

(1)
All

(2)
Below
1 year

(3)
Ages
1–9

(4)
Ages
10–19

(5)
Ages
20–34

(6)
Ages
35–44

(7)
Ages
45–54

(8)
Ages
55–64

(9)
Ages
65–74

(10)
Ages
75⫹

2.81010.1998(0.0168)7.1

1.8216
0.0923(0.0753)
5.1

0.06630.0047(0.0046)
7.1

0.1220⫺0.0116(0.0047)⫺9.5

0.2293⫺0.0254(0.0052)⫺11.1

0.3619⫺0.0051(0.0108)⫺1.4

0.94520.0490(0.0202)5.2

2.30070.1751(0.0304)
7.6

4.83850.2915(0.0533)
6.0

10.4830
1.2721(0.0983)12.1

0.0044(0.0016)⫺0.0003(0.0075)
0.1391(0.0116)0.0492(0.0056)⫺0.0126(0.0024)⫺0.0072(0.0019)0.0049(0.0020)0.0154(0.0095)
0.0063(0.0047)0.0090(0.0096)0.0344(0.0174)⫺0.0026(0.0038)——0.0001(0.0005)

0.0001(0.0009)
0.0001(0.0005)
0.0005(0.0009)
0.0023(0.0012)⫺0.0013(0.0015)0.0001(0.0001)
0.0002(0.0002)0.0005(0.0004)
0.0004(0.0014)⫺0.0004(0.0009)⫺0.0001(0.0008)⫺0.0140(0.0031)0.0007(0.0017)0.0003(0.0002)⫺0.0002(0.0008)
0.0001(0.0015)⫺0.0014(0.0015)
0.0021(0.0008)⫺0.0166(0.0027)⫺0.0061(0.0022)0.0003(0.0005)

0.0008(0.0018)⫺0.0053(0.0041)0.0108(0.0058)
0.0012(0.0016)0.0012(0.0029)⫺0.0062(0.0031)⫺0.0003(0.0014)⫺0.0036(0.0019)0.0136(0.0099)
0.0377(0.0125)
0.0093(0.0043)⫺0.0023(0.0030)⫺0.0008(0.0034)0.0017(0.0024)0.0031(0.0028)0.0322(0.0178)
0.0965(0.0221)0.0246(0.0084)0.0041(0.0029)⫺0.0073(0.0034)
0.0025(0.0045)0.0083(0.0050)⫺0.0506(0.0269)0.2107(0.0418)
0.0635(0.0165)⫺0.0010(0.0039)⫺0.0059(0.0038)0.0107(0.0068)

0.0288(0.0085)0.0106(0.0343)0.7997(0.0707)0.2846(0.0365)
0.0054(0.0054)⫺0.0032(0.0049)
0.0233(0.0131)

Notes: Standard errors clustered by county are reported in parentheses. Entries are estimates of the cumulative effect of cold temperature on mortality over thirty days. Each column reports estimates from theage-specific model listed in the column heading. For the all-age model, the dependent variable is the all-cause or cause-specific mortality rate, age-adjusted to the 1980 gender-specific population standard. Eachrow corresponds to a specific cause of death. We report the thirty-day cumulative effect corresponding to days with temperature below 30°F.

from other causes. This implies that the interpretation of ourestimates by specific cause of death is complicated, and theregressions coefficients could be biased in ways that are
difficult to predict.
D. Dynamic Effect by Income and Robustness Checks

Table 6 reports estimates from alternative specifications
and approaches. In table 6, we first consider models withlonger lag windows. Then we consider models where theeffects of cold temperature interact with income. We areinterested in investigating whether the effect of a cold day islarger in counties that are poorer. We then provide two testsof the acclimatization hypothesis, which in essence suggestthat the temperature-mortality relationship may vary acrossgeographical areas. First, we examine whether the cold
temperature effects differ with the average exposure to cold

TABLE 6.—ESTIMATES FROM ALTERNATIVE SPECIFICATIONS
Females

1. Models with longer lag window
60-day window(s.e.)
90-day window(s.e.)
2. Models estimated by income subgroups
10% poorest counties(s.e.)
10% richest counties(std error)
Remaining 80% of counties(s.e.)
3. Models estimated by average exposure to cold days
Counties with 10 days or fewer of cold temperature per year
(s.e.)Counties with 90 days or more of cold temperature per year
(s.e.)Counties with 11–89 days of cold temperature per year
(s.e.)
4. Relative temperature models: Impact of 1 day with:
Mean temperature 10 degrees below county monthly mean
Mean temperature 20 degrees below county monthly mean

Males

Estimate

Percent Effect

Estimate

Percent Effect0.3079(0.0197)
0.3723(0.0250)13.4

0.1633(0.0219)0.1722(0.0278)5.8

0.1671(0.1751)0.2517(0.0716)0.2638(0.0262)7.3

0.5801(0.1748)0.1717(0.0743)
0.1888(0.0268)20.6

0.6823(0.1826)
0.0966(0.0549)0.2040(0.0174)24.3

0.2335(0.0241)0.4333(0.0909)

8.3

16.2

11.0
11.5

0.5482(0.1360)
0.2195(0.0431)0.2563(0.0160)

23.9

0.2595(0.0206)
0.5298(0.0781)

11.3

9.6
11.2

23.1

6.1

6.1
6.7

3.4
7.3

15.4

Notes: Standard errors clustered by county are reported in parentheses. Each column is from a separate regression. The dependent variable is daily mortality rate, age-adjusted to the gender-specific 1980 population
standard. All models include a series of county-by-year-by-month effects. We report the thirty-day cumulative effect corresponding to days with temperature below 30°F.

EXTREME WEATHER EVENTS, MORTALITY, AND MIGRATION

673

days for the county. Second, we quantify the impact of
exposure relative to the county normal rather than theimpact of absolute temperature thresholds. The idea is that
one day below 30°F in Florida and Minnesota might nothave the same effect on mortality or that the cold temperature thresholds vary across geographical areas because
human bodies get acclimatized to cold or hot temperatures(see Eurowinter Group, 1997).
The baseline specifications in tables 3 to 5 include only
thirty lags and therefore implicitly assume that any effectoccurs within one month of the temperature shock. We havealso estimated models with longer lag structures with up toninety days of lag effects in order to capture the dynamics
of longer horizons. The estimates are reported in panel 1 oftable 6. The estimates for females are larger when we
consider longer horizon. For males, the longer window
estimates are marginally smaller than those reported intables 4 and 5. However, the differences are small relative tothe sampling variability in the estimates. Based on thisevidence, we conclude that a thirty-day window provides areasonable choice of lag window.25 For males, the full
impact of a cold day on mortality occur well within thirtydays for males. For females, a window of larger horizon
yields mortality impacts that are 40% to 50% larger. However, because the computational difficulty increases rapidly
with the lag structure and for comparability with the models
for males, we will continue using the baseline specificationof a thirty-day lag window.
The estimates in panel 2 pertain to different income
subgroups of the sample. In order to gauge the impact ofincome on the impact of cold temperatures on mortality, we
stratify the analysis for three groups of counties. The regression models were estimated separately on the 10%
poorest counties in our sample (based on real per capita
income), the 10% richest counties, and the remaining 80%
of counties whose per capita income falls between the tenthand ninetieth percentiles of the national distribution. Again,
there are striking differences across gender. For males, thepoint estimates indicate that the mortality impacts are larger
in the poorest counties. For these counties, one day of cold
temperature increases the daily mortality by 0.5801 deaths
per 100,000 residents. The impact for the richest counties is
the smallest, at 0.1888 deaths per 100,000, and the impact
for the remaining counties is 0.1717. Thus, it appears thatfor men, there are differences in the impact of cold temperatures on mortality due to income and that the relationshipis nonmonotonic as the impact in the richest counties ispractically the same as among the counties in tenth toninetieth percentile range. Remarkably, no such differential
impact by income strata are found for females. The threepoint estimates are all within sampling error of each other.
In panel 3, we consider models that are estimated separately for counties that vary in their average exposure tocold days in the typical year. In particular, we consider
counties that experience ten or fewer cold days per year andninety or more cold days per year (the national average isforty days per year in which daily mean temperature falls
below 30°F). This allows us to investigate the acclimatization hypothesis, which predicts that the mortality impacts
should be smaller in counties that face more cold days per
year, because residents and public authorities are better
prepared to deal with cold weather. The evidence suggeststhat individuals get acclimatized to cold temperatures. The
mortality impact of cold temperature is remarkably larger incounties that experience ten or fewer cold days per year. For
such counties, the mortality impacts are 0.5482 per 100,000
females and 0.6823 deaths per 100,000 males. The mortality
impact is smaller in counties that are exposed to at least
ninety cold days per year in the typical year. Nevertheless,the impact of cold temperature on mortality remains sizable
and individually significant. In general the standard errors
for the point estimates in panel 3 are larger than thecorresponding standard errors reported in tables 3 to 5. As
such, none of the differences between the panel 3 estimates
and the tables 3 to 5 estimates appears large in light of theassociated sampling errors, thus weakening the support for
the acclimatization hypothesis.
Panel 4 in table 6 examines the possibility that relative
exposure (as opposed to absolute exposure) is what mattersin the temperature-mortality relationship. So far, the models
we considered specify an absolute relationship between
temperature and mortality. In other words, in the specification analyzed in tables 3 to 5, cold temperature is definedindependent of counties. This could be inappropriate under
the hypothesis that there is acclimatization. In that case,
exposure relative to the county normal could be a better
predictor of mortality. Moreover, areas with relatively warm
climates with low fluctuations in temperatures, such as
southern California, will contribute little or no identifying
variation to the models.26 In order to take this possibility
into account, we define cold days as those where thetemperature falls 10 or 20 Fahrenheit degrees below thecounty mean for the month of observation. For example, inthe case of a 10 degree variation, the temperature variables
used in the regressions are defined as T cdt ⫽ (Temperature cdt ⫺ Mean Temperature cm ⬍ 10). The results fromthis relative effect model obtained estimating with these
new temperature variables a model that includes fixed
effects are reported in panel 4 of table 6. Remarkably, theestimates appear similar or even larger than the baseline
estimates. For example, the thirty-day cumulative effect of
one day where the temperature is 10°F below the county
mean for the month of observation increases the daily
mortality rate by 8.3% and 11.3% for males and females,
respectively. These are slightly larger than what we estimate
from the absolute effect models. When we consider the

25 In the typical year for the United States as a whole, only thirty cold
waves last longer than thirty days.

26 For example, over our sample period 1972–1988, San Diego County
had no days when the mean temperature fell under 30°F.

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THE REVIEW OF ECONOMICS AND STATISTICS
TABLE 7.—ROBUSTNESS ANALYSIS
FemalesA. Models for log mortality rate
30-day cumulative effect (impact in deaths per 100,000)(s.e.)
B. Models controlling only for temperature
30-day cumulative effect(s.e.)
C. Models with interactions on number of cold days in last 7 days
30-day cumulative effect(s.e.)
D. Models estimated on subsamples
30-day cumulative effect, pre-1980(s.e.)
30-day cumulative effect, 1980 and after(s.e.)
E. Models based on minimum and maximum temperatures only
Daily maximum ⬍⫽30°F(s.e.)
Daily minimum ⬎⫽ 80°F
(s.e.)
F. Models based on county of residence
30-day cumulative effect(s.e.)
G. Models without first and last two days of months30-day cumulative effect(s.e.)

Males

Estimate

Percent Effect

Estimate

Percent Effect0.2735(0.0212)11.9

0.2208(0.0239)7.9

0.2560(0.0149)11.1

0.2015(0.0167)7.2

0.3831(0.0248)16.7

0.2877(0.0248)10.2

0.1667(0.0247)
0.3373(0.0205)

7.3

0.1395(0.0281)0.2563(0.0215)

5.0

0.1982(0.0230)⫺0.1389(0.1133)

7.1

14.7

9.1

0.2402(0.0200)
0.1356(0.1662)10.5

0.2830(0.0169)

12.3

0.2192(0.0156)7.8

0.2594(0.0178)

11.3

0.2344(0.0204)

8.3

5.9

⫺4.9

Notes: Standard errors clustered by county are reported in parentheses. Each column is from a separate regression. The dependent variable is daily mortality rate, age-adjusted to the gender-specific 1980 population
standard. All models include a series of county-by-year-by-month effects. We report the thirty-day cumulative effect corresponding to days with temperature below 30°F.

relative effect model with a deviation of 20°F, the estimateddynamic causal effect increases dramatically to 23.1% and15.4%, essentially doubling what was reported earlier. The
fact that the estimates from the relative models point tolarge and significant effects of cold temperature exposure on
mortality is greatly reassuring since it implies that ourbaseline estimates in tables 3 to 5 are not driven by the
choice of a particular model of the temperature-mortality
relationship.
In table 7, we have examined a series of alternative
specification intended to further probe the robustness of ourbaseline estimates. First, we have reestimated our models
using the log daily mortality rate in order to assess theimportance of nonlinearities in the mortality-temperature
relationship. The normalized impacts from these models
listed in panel A are marginally larger than those reported in
the baseline specifications. Second, we report estimatesfrom models that drop the controls for daily precipitation.
This leads to unchanged estimates. In panel C, we consider
specifications with interaction between current and lagged
temperatures and the occurrence of multiple days of cold
temperatures. Namely, the models include an interaction
between each main temperature effect and the number ofcold days in the past thirty days. This specification leads toslightly larger mortality impacts than the baseline specification. Next, in panel D, we have tested whether theestimated effects are different in the second half of theperiod (1980–1988) relative to the first half (1972–1979).
The results indicate that if anything, the cold-related mortality impacts are larger in the last half of the sample, though

the difference between the two periods appears marginallystatistically significant. Next we have estimated models
based on daily minimums (for high temperature) and daily
maximums (for cold temperature) rather than the daily
average temperature. The estimates are in panel E, and few
differences are noticeable. In panel F, we define our samples
on the basis of county of occurrence rather than the countyof residence. The estimates are not sensitive to this change.
Finally in panel G, we want to make sure that our results do
not reflect something mechanical that has to do with certain
specific dates. We constructed a new sample where wedropped all 0–2 days from the beginning and the end of themonth, as well as January 1, October 31, and late December.
Our estimates do not seem to be sensitive to this sample
selection. Taken as a whole, the evidence in table 7 clearly
demonstrates that none of these considerations alters themain conclusions drawn from the analysis in tables 3 to 5.
V.

Effect of Cold Weather on Life Expectancy

In section IV, we showed that episodes of extreme cold
are associated with permanent increases in mortality. In thissection, we ask, How large is the effect of cold temperature
exposure on life expectancy?27 In particular, in section VA,
we ask what would happen to life expectancy in the absenceof exposure to extreme cold episodes. We answer thisquestion for the United States as a whole and for some
27 We focus only on cold-related mortality since our results suggest that
hot temperature causes only near-term displacement of mortality, therefore
not leading to significant reductions in life expectancy.

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675

TABLE 8.—NUMBER OF DEATHS CAUSED BY COLD TEMPERATURE AND YEARS OF LIFE LOSTAge Group

(1)
(2)
(3)
(4)
(5)
White Population in 2000 Cumulative Effect of One Cold Day Implied Annual Years of Life Lost Total YLL(in 100,000)
on Mortality per 100,000
Deaths(2000)

Females
0
14.3
1–9
133.4
10–19
153.3
20–34
226.3
35–44
182.5
45–54
189.2
55–64
107.2
65–74
87.1
75⫹
94.5
Annual female deaths attributable to cold temperature (all ages)
Males
0
15.1
1–9
140.6
10–19
162.7
20–34
238.0
35–44
184.4
45–54
156.8
55–64
100.6
65–74
73.1
75⫹
55.4
Annual male deaths attributable to cold temperature (all ages)

0.02710.00660.0006⫺0.0004
0.00760.02890.06950.18392.3030

15.5
35.2
3.7
⫺3.6
55.5
218.7
298.0
640.7
8,705.3
9,969

80.0
76.4
66.5
51.9
43.2
32.9
24.1
16.2
9.6
YLL per death:

1,240.1
2,690.6
244.7⫺187.92,396.7
7,195.7
7,182.2
10,379.5
83,571.3
11.5

0.09230.0047⫺0.0116⫺0.0254⫺0.00510.04900.17510.29151.2721

55.7
26.4
⫺75.5
⫺241.8
⫺37.6
307.3
704.6
852.3
2,819.0
4,411

74.8
71.3
61.5
47.3
38.0
29.0
20.8
13.7
8.1
YLL per death:

4,170.0
1,884.7⫺4,642.8⫺11,437.5⫺1,429.5
8,912.5
14,655.7
11,677.1
22,833.7
10.6

Notes: We begin by multiplying the white population in that age group in 2000 (column 1) by the age-specific estimate of the effect of one cold day on mortality (column 2). The product of columns 1 and 2
times 40 (the annual number of cold days for the typical county) provides an estimate of annual deaths associated with cold shocks (column 3). The product of column 3 by the years of life lost per death in eachage group in column 4 represents the number of years of life lost per death caused by cold temperature (column 5). Finally, we divide column 5 by the total number of deaths attributable to cold temperature toobtain the number of years of life lost per death caused by cold temperature (YYL per death).

selected cities. Second, in section VB, we ask what fractionof the gains in life expectancy experienced by the U.S.
population over the past thirty years can be attributed tolower exposure to extreme cold due to the secular movement of the U.S. population from cold states toward warm
states. Finally, in section VC, we test whether mobility
decisions of individuals appear to be sensitive to the longevity benefits associated with avoiding extreme cold.
A. Years of Life Lost Due to Cold Weather

In table 7 we calculate the number of annual deaths forfemales and males caused by cold weather and the corresponding years of life lost (YLL) per death. We begin bymultiplying the 2000 population counts in each age group(column 1) by the age-specific estimate of the cumulative
thirty-day effect of one cold day on mortality (column 2).
The product of columns 1 and 2 is then multiplied by 40,
which is roughly the annual number of cold days for the
typical county (defined as days where the mean temperature
falls below 30°F), to obtain an estimate of annual deaths
associated with cold shocks (column 3).28 For males, theestimates range from ⫺241.8 for the 20–34 age group to
2,819.0 for the 75⫹ age group.29 For females, the impliedannual deaths due to cold temperature are positive in all but
one of the age categories.
28 For simplicity, this estimate assumes uniform distribution of population across all counties.
29 As we demonstrated in section IV, the negative effect on middle-age
individuals is mostly driven by a reduction in car accidents.

As a whole, 14,380 annual deaths are attributable to cold
temperature in the United States, which corresponds toapproximately 0.8% of annual deaths (based on the 2000
mortality total for whites). We interpret this figure as aremarkably large number. For example, this total exceeds
the annual deaths due to leukemia, homicide, chronic liverdisease or cirrhosis, and other important causes of death.
The gender difference in these cold-related deaths is equallyremarkable: the implied mortality impact is basically twiceas large for females than males. Most of this difference
comes from the predicted impacts for 75⫹ age group.
Column 4 displays the years of life lost per death in eachage group based on the 2000 life tables for white males andfemales.30 We multiply these years of life lost (column 4) by
the number of implied deaths in each age group (column 3).
The resulting figure (column 5) corresponds to the total
number of years of life lost caused by cold temperature. For
both males and females, the age group most affected is the75⫹ group, which loses a combined 106,405 years of life
annually because of exposure to cold temperature. Again,
this loss disproportionally affects women.
Finally, we divide the YLL in column 5 by the total
number of deaths attributable to cold temperature to obtainthe number of years of life lost per death caused by cold
temperature (YYL per death). The estimate is substantial:
the average person who died because of cold temperature
exposure lost in excess of ten years of potential life. This
30 These data are available at http://www.cdc.gov/nchs/data/lt2000.pdf.

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THE REVIEW OF ECONOMICS AND STATISTICS
TABLE 9.—DEATHS CAUSED BY COLD TEMPERATURE AS A FRACTION OF TOTAL DEATHS, BY MSA
MSA

Population 65⫹(2000 Census)

Annual Deaths(2000 MCOD)

Annual Cold Days

Implied Deaths% of Actual Deaths

ChicagoPhiladelphia
New York
Los Angeles
Tampa Bay
DetroitBostonPittsburgh
Phoenix
San JoseRiverside
Washington, DC
Minneapolis
Cleveland
San Diego
Atlanta
BaltimoreWest Palm Beach
HoustonFort Lauderdale
Total

547,349
487,064
471,567
431,491
374,409355,812344,072
335,190
300,451
265,335
248,503
246,401
236,316
231,183
217,698
215,797
210,128
203,432
201,481180,062
6,103,741

37,95331,72039,41434,202
21,454
23,17839,084
20,914
17,153
15,92915,722
15,462
13,997
14,914
10,267
13,905
12,977
13,237
12,36911,738415,589

57
31
36
0
0
69
50
47
0
5
0
28
109
56
0
9
28
0
1
0
—

542
263
296
0
0
426
299
271
0
24
0
121
448
224
0
32
103
0
4
0
3,054

0.014
0.008
0.008
0.000
0.000
0.018
0.008
0.013
0.000
0.002
0.000
0.008
0.032
0.015
0.000
0.002
0.008
0.000
0.000
0.000
0.007

Note: This table focuses on the population age 65⫹ and on the twenty metropolitan areas with the largest number of elderly white residents.

simple calculation highlights the fact that cold temperaturecauses nontrivial reductions in expected lifetime.
It is important to realize that this estimate of counterfactual longevity depends on the assumption that people whodied because of a cold wave would have lived until theaverage life expectancy for their age and gender. One
important caveat to this calculation is that it may overstate
the loss in life years, because the affected individuals may
have been negatively drawn from the health distribution.
While we account for heterogeneity in age and gender, we
are unable to account for other determinants of health.31 It istherefore possible that the affected individuals have shorter
life expectancies than the average person in their age-gendergroup.
Of course, this effect varies tremendously dependingon geography. Table 9 examines cold-related deaths bycity among the elderly. In this table, we focus on the
population of age 65 and above since it is the mostaffected by cold temperature. In addition, most individuals in this population are retired and face fewer constraints in their mobility decisions that prime-aged adults.
We focus on the twenty largest metropolitan statisticalareas (MSAs) in terms of elderly white population.32 The
Chicago MSA is the largest, with an elderly population of
547,349, and the Fort Lauderdale MSA is twentieth, witha population of 180,062. The second column shows thetotal annual deaths for each MSA. Interestingly, the total
mortality rankings do not exactly correspond to the
population rankings. For example, New York City has thelargest mortality total in the white elderly group (39,414)
31 Unfortunately, the 1972–1988 MCOD files contain little usable demographic information besides age and gender. For example, educational
attainment is added to the MCOD files starting in 1989.
32 We use data from the 2000 Census.

but ranks third in population.33 The next column shows
the average annual number of cold days in each metropolitan area (as before, defined as days where the mean
temperature falls below 30°F). For example, Chicago is
exposed to 57 cold days per year on average, while
Philadelphia faces only 31. The city with the strongest
exposure is Minneapolis, with an average of 109 cold
days per year. Several cities experience no or few colddays, including Los Angeles, Tampa Bay, Phoenix, and
San Jose.
A simple counterfactual exercise is to ask how many
deaths would be delayed if all the elderly in a cold city
moved to a city where they would not be exposed to cold
temperature (for example, Los Angeles). The answer isprovided in column 4, which shows the implied annual
deaths due to cold temperature in each metropolitan area.
This is obtained by the product of columns 1 and 3 (theexposure) multiplied by 1.74, the estimated impact of 1 cold
temperature day on deaths per 100,000 in the 65⫹ population.34
The Chicago MSA has the most annual cold-related
deaths, 542, followed by Minneapolis (448) and Detroit(426). For the twenty MSA as a whole, 3,054 deaths, or
0.7% of all deaths in these cities, could be delayed bymoving individuals to areas not exposed to cold temperature. The last column shows the city-specific impacts inpercentage terms. This is obtained by taking the ratio of
33 Of course, these differences cannot be interpreted causally, as theymight reflect differences in the age distribution above 65 or socioeconomic differences across cities. Remarkably, this estimate was basicallythe same for males and females (1.7428 and 1.7430 respectively).
34 This estimate is obtained from estimating our distributed lag regression (3) for the population aged 65 and above. It roughly corresponds toa population-weighted average of the age-specific estimates reported intable 5.

EXTREME WEATHER EVENTS, MORTALITY, AND MIGRATION

implied deaths to total deaths. The results show that forsome cities, cold-related deaths represent a sizable fractionof actual deaths. For example, in the Minneapolis MSA, ourestimate of cold-related mortality corresponds to 3.2% of alldeaths. Other affected MSAs are Detroit (1.8%), Chicago(1.4%), and Cleveland (1.5%).
B. Gains in Life Expectancy Due to Secular Trends in
Mobility

We now turn to geographical mobility. Over the past
half-century, the U.S. population has moved from the northeastern and midwestern states to southwestern states, movement that has resulted in a diminished exposure to cold
temperature. We compute how much of the observed increase in life expectancy can be attributed to the secular
movement of the U.S. population from cold areas in the
North to warmer areas in the Southwest.
Over that thirty-year period, the average age of death in
the white population increased by 8.1 years for females and6.3 years for males. How much of this improvement can be
attributed to lowered exposure to extreme cold caused bygeographical mobility? We look at all U.S.-born individuals
who live in a state different from the state of birth. For eachof these movers, we compare the exposure in the state ofresidence with the counterfactual exposure that that individual would have experienced in the state of birth.35
Our estimates indicate that on net, 4,600 deaths aredelayed by the changing exposure to cold temperature eachyear (s.e. ⫽ 2,091.0). This figure is the net effect ofmobility, because it is the difference between the lower
mortality experienced by those who moved from cold states
to warm states and the higher mortality experienced bythose who moved in the opposite direction. We calculate
this difference for each state pair and age group.
When we multiply this difference by the estimated number of years of life lost associated with a cold day for the
relevant age group, we find that the average age of death (orlongevity) increased by 0.008 to 0.015 years per calendar
year as a result of lower exposure to cold weather due tomigration. In other words, U.S. residents gained about fourdays of extra life per calendar year because of mobility. The
details of the calculation are presented in the appendix.
We compare this figure to the annualized increase inlongevity in the United States over the period 1970–2000. In
annual terms, the average age of death in the white population increased by 0.20 to 0.25 years per calendar year overthe past thirty years. Assuming that the age distribution ofmovers across states is constant over time, we can compare
our estimated longevity effect of mobility to the annualized
increased in overall longevity in the United States between
35 To identify movers, we use census data. Since we know only state ofbirth, not county of birth, we compute the change in exposure as thedifference between the number of cold days in the state of residence minus
the number of cold days in the state of birth, thus ignoring within-state
differences in weather.

677

1970 and 2000. Our estimate of the longevity effect ofmobility corresponds to approximately 3% to 7% of theseannual gains in overall longevity. We view this as a remarkably large effect.
There are two important caveats to the interpretation ofthis relationship. First, one might expect that people migrating from colder areas to warmer areas are those most at risk
in cold areas. This would cause us to understate the effect of
migration.
But in the calculations about the potential magnitude ofthe effects on mobility on life expectancy, we are implicitly
holding relative prices fixed.36 This assumption is not fully
realistic, because the large number of movers who have left
the Northeast and the Midwest to settle in the West andSouth is likely to have affected wages and land prices
throughout the United States. In particular, one might expectthat in the absence of the vast migration of the past forty
years from cold states to warm states, wages in cold states
would have been lower (or at least not higher) and land
values would have been higher (or at least no lower),
everything else constant. The opposite would be true inwarm states. For individuals who do not own land, thiswould imply a lower standard of living in cold sates and ahigher standard of living in warm states. While it is hard toknow exactly what this may have implied for overall life
expectancy, this general equilibrium effects have the potential to bias our counterfactual estimates of longevity.
C. The Decision to Move and Cold Temperatures

We now test whether individual mobility decisions arecorrelated with the health benefits associated with avoiding
extreme cold. This analysis is meant to be descriptive andhelps contextualize the main findings of the paper. Although
our findings are consistent with a model of rational mobility,
we cannot account for many unobserved determinants ofmobility, and therefore the correlations uncovered in thissection do not necessarily have a causal interpretation.
Table 10 shows estimates of the impact of differential
exposure to cold weather on the probability of moving, byage. The dependent variable is a dummy equal to 1 if therelevant individual in the 2000 Census resides in a state
different from their state of birth.37 The main independent
variable is the interaction between the difference in thenumber of cold days and the relevant age group. Temperature is measured at the state level. The first entry in column
1 indicates that the probability of moving from state i tostate j increases if state i has fewer cold days than state j.
This probably reflects the secular movement toward warmer
locations.
36 The reason is that our estimates of the effect of weather on mortality
are obtained using within-area, short-run changes in weather. Theseestimates therefore hold everything about the county, including prices,
fixed.
37 We include only white males and females born in the forty-eightcontinental states and the District of Columbia.

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THE REVIEW OF ECONOMICS AND STATISTICS
TABLE 10.—ESTIMATES FROM MOBILITY MODELS, MOBILITY DEFINED BASED ON STATE OF BIRTH(DEPENDENT VARIABLE: MOBILITY INDICATOR (⫽1 MOVED FROM STATE OF BIRTH))

Difference in annual cold days
Difference in annual cold days ⫻ Age
35–44
45–54
55–64
65–74
75⫹
F-statistics
Interactions ⫽ 0Interactions all equal
Age dummies
Demographic controls
State birth effects
State residence effects

(1)

(2)

(3)

(4)

(5)

(6)

⫺0.0077(0.00002)⫺0.0064(0.00004)⫺0.0061(0.00003)⫺0.0068(0.00003)⫺0.0048(0.00003)⫺0.0037(0.0011)

—
—
—
—
—

—
⫺0.0006⫺0.0013⫺0.0025⫺0.0029—⫺0.0006⫺0.0012⫺0.0024⫺0.0028—⫺0.0005⫺0.0012⫺0.0023⫺0.0026—⫺0.0003⫺0.0006⫺0.0013⫺0.0015—⫺0.0004⫺0.0008⫺0.0015⫺0.0018

—
—
No
No
No
No

1,383.2
1,122.2
Yes
No
No
No

1,306.5
1,075.2
Yes
Yes
No
No

1,222.5
991.6
Yes
Yes
Yes
No

481.4
408.6
Yes
Yes
No
Yes

621.2
495.1
Yes
Yes
Yes
Yes

Note. Robust standard errors are reported in parentheses. Entries are estimates of the impact of differential exposure to cold temperature on the probability of moving, by age. The dependent variable is a dummy
variable equal to 1 if the relevant individual resides in a state different from his or her state of birth in 2000. The level of analysis is the individual, and the data are from the 2000 Census of Population. In the sample
are white males and females, born in the forty-eight continental states and the District of Columbia. The independent variable in column 1 is the difference in the number of cold days between the state of residence
and the state of birth. In column 2, we interact the difference in the number of cold days with indicators for each age group. For example, for individuals 35 to 44, a one-day decline in the number of annual cold
exposure is associated with an increase in the probability of mobility by .0008. For individuals above age 75, a one-day decline in the number of annual cold exposure is associated with an increase in the probabilityof mobility by 0.32 percentage points. In column 3 we control for a full set of demographic variables, including sex, educational attainment, marital status, family size, work disability, weeks worked, and total income.
In column 4 we include unrestricted effects for state of birth, and in column 5 we include unrestricted effects for state of residence. The model in column 5 is close to be fully saturated.

What is more interesting is that the magnitude of thiseffect is different across age groups. In particular, in column
2, we interact the difference in the number of cold days withindicators for age groups. The entries in column 2 indicatethat the magnitude of the effect of cold weather on mobility
increases with age (in absolute value), after controlling forage dummies. For example, for individuals ages 35 to 44,
the probability of mobility is only marginally affected by thedifference in exposure to cold. A one-day decline in thenumber of annual cold exposure is associated with anincrease in the probability of mobility by 0.0008. By contrast, the effect is four times larger for individuals above age
75: a one-day decline in the number of days of annual cold
exposure is associated with an increase in the probability ofmobility by more than three-tenths of a percentage point.
The key point here is that the pattern of the age-specificcoefficients mirrors differences across age groups in theeffect of cold weather on mortality uncovered in table 5.
In columns 3 to 6, we include an increasing number ofcontrols. In column 3, we add a full set of demographic
variables: sex, educational attainment, marital status, family
size, work disability, weeks worked, and total income. All ofthese are controlled for using a series of unrestricted dummy
variables. In column 4, we include dummy variables forstate of birth, and in column 5, we also include dummy
variables for state of residence. The model in column 5 isclose to fully saturated, and it fully accounts for permanentdifferences across states of births and state of residence, as
well as age effects and demographics. The coefficients on
the interactions are generally lower. Notably, the differences
across age groups persist. The coefficient for the groups
above age 75 remains about four times larger than thecoefficient for the age group 35 to 44.
To give a more precise idea of the relationship between
migration choice and longevity gains, in figure 6 we plot theage- and gender-specific estimates of cold temperature mortality impacts (x-axis) against the corresponding age- andgender-specific estimates of the effect of the difference incold days on the probability of mobility (y axis). The effectsof the difference in cold days on the probability of mobility
are the coefficients (in absolute value) on the interaction
between the difference in cold days and age dummies in amodel similar to the one in column 6 of table 10, where theinteractions are separately estimated by gender. Note thatthese coefficients are not the mobility rates by age (whichmechanically increase with age, and therefore are mechanically positively correlated with longevity gains). Instead,they represent the sensitivity of the mobility of different age
groups to differences in cold days. Each observation is an
age-gender group, where age groups are 35–44, 45–54,
55–64, 65–74, and 75⫹.
The figure shows that the age-gender groups that have themost to gain in terms of additional longevity caused by
reduced exposure to cold are the ones whose mobility is themost sensitive to differences in annual cold days between
localities. It is clear that the two variables are positively
correlated. A regression of the age- and gender-specificmobility effects on the age- and gender-specific cold temperature mortality impacts yield a coefficient equal to.00010 (.00003), and an R 2 equal to .54. The coefficient formales is .0011 (.0004), and R 2 is .68. The coefficient for
females is .00010 (.00006), and R 2 is .48. Based on thisfinding, we conclude that individual mobility decisions
appear to be correlated with the health benefits of avoiding
exposure to cold weather shocks, even after controllingfrom where they were born, where they live, and an exhaustive list of mobility predictors.38
38 We note a possible alternative interpretation of the results in thissection. It is in theory possible that the introduction of air conditioning is

EXTREME WEATHER EVENTS, MORTALITY, AND MIGRATION

679

FIGURE 6.—RELATIONSHIP BETWEEN THE EFFECT OF COLD DAYS ON MOBILITY AND THE EFFECT OF COLD TEMPERATURE ON MORTALITY
Male
Fitted values

FemaleEffect of Cold Days on Mobility

.004

.002

0

−.002
10

0

20

Effect of Cold Days on Mortality
Notes: The x axis represents age- and gender-specific estimates of cold temperature mortality impacts. The y axis represents age- and gender-specific effects of differences in number of cold days on mobility(see coefficients on interaction term in column 6 in table 9). Age groups are 35–44, 45–54, 55–64, 65–74, 75⫹.

VI.

Conclusion

Our findings indicate that increases in mortality causedby cold temperature are long lasting. We find evidence of alarge and statistically significant permanent effect on mortality of cold waves. By contrast, the increases in mortality
associated with heat waves are short lived. The increase inmortality that occurs in the days immediately following heatwaves appears entirely driven by temporal displacement.
The aggregate effect of extreme cold on mortality islarge. We estimate that the number of annual deaths attributable to cold temperature is about 0.8% of annual deaths in
the United States during the sample period. This effect issignificantly larger among males living in low-income areas.
The main contribution of this paper is to document theimportance of a previously unrecognized determinant ofgains in life expectancy in the United States. Over the past
several decades, the U.S. population has moved from thenortheastern and midwestern states to the southwesternstates. This secular trend has resulted in a diminishedexposure to cold weather. We calculate that every year,
4,600 deaths are delayed by the changing exposure to cold
temperature. Such effect on longevity accounts for 3% to
7% of the overall increase in longevity experienced by the
U.S. population over the past thirty years.
responsible for the mobility patterns uncovered above, under some assumptions. Specifically, if counties that have few days of extreme cold arealso counties that before air conditioning had unpleasantly hot summers,
and if age groups that benefit more from fewer days of cold are also thegroups that benefit more from air conditioning, one might observe therelationship between mobility and cold days documented in table 10 evenin the absence of any causal effect of cold weather on mobility.

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A.

Step 1: Death Rates and Conditional Mortality Probabilities

Since the mobility patterns are tabulated from the 2000 Census, we also
compute mortality rates and probability from the 2000 Multiple Cause of
Death (MCOD) files. We estimate the age-specific death rates for eachstate as:R aj ⫽

冘D aj
49a ⫽ 0, 1,. . . , 100,

N ajk

k⫽1

where D aj is the number of deaths occurring at age a in state j. In otherwords, R aj is simply the ratio of the number of deaths at a given age, tothe population of that age in a state. In the case where the age ⫻state-specific death rate is exactly 0 (which occurs when no deaths occur
at a given age in a state), we use the national death rate for that age.39
Conditional mortality probabilities are also computed from the data in the2000 MCOD file. We consider ages 0 to 100 and compute the probabilities
at the national level.
Let D a denote the number of deaths at age a. The share of total deaths
at age a, F a , is defined as
Fa ⫽

冘

Da
100
a⫽0

a ⫽ 0, 1, . . . , 100
Da

Given survival to age m, the conditional probability of dying at age (a ⬎m) is given by
P a兩m ⫽

冘

Fa

.

100
i⫽m⫹1

Fi

Note that for a given survival age m, ¥ a P a兩m ⫽ 1. By construction P a兩m ⫽0 for a ⱕ m. For the last age group (when m ⫽ 100), this probability is
not defined, so we assume that no one lives past 100.
B.

Step 2: Affected Number of Migrants

First, we calculate the expected annual number of migrants deaths at
age a. This is obtained by multiplying the number of migrants of age a instate j by the age-specific death rate in state j (so that we are assuming thatthe same death rates apply to both migrants to state j and residents bornin state j):冘49

E aj ⫽

R aj ⫻ 共N ajk ⫺ N ajj 兲,k⫽1where E aj ⫽ expected annual number of migrant deaths in state j, at age
a. For the United States as a whole, approximately 700,000 migrant
deaths were expected in 2000. There is substantial variation across statesin the expected number of migrant deaths, which reflects differences
across states in the number and age distribution of migrants and inage-specific mortality rates. For example, the unadjusted standard deviation in the number of annual expected migrant deaths is 19,100. The stateswith the highest totals are California and Florida, and the lowest totals arefor Washington, DC, and North Dakota.
From this, we calculate the affected number of migrant deaths—theannual number of migrant deaths attributable to (mobility-induced) differential exposure to cold weather shocks:

APPENDIX A冘49

Calculation of Longevity Gains
This appendix describes in detail the calculation of the longevity gains
in the paper. First, we note that every calculation is done separately formales and females. In order to better describe our procedure, we definesome notation. Let N ajk denote population of age a, residing in state j,born in state k. The differential exposure to cold weather shocks is definedas S jk ⫽ number of annual cold weather days in state j ⫺ number ofannual cold weather days in state k. Note that by construction, S jj ⫽ 0.

A aj ⫽

E aj ⫻ ␤ a ⫻ S jk /365.25,

k⫽1

where ␤ a is the dynamic causal effect of a cold weather day on daily
mortality rates for age group a, taken from table 5. Since we calculate the
39 For the 49 states and the 101 ages in our data, imputation is requiredfor 36 of 4,949 state ⫻ age pairs.

EXTREME WEATHER EVENTS, MORTALITY, AND MIGRATION
affected number of migrants deaths by single year of age, we assign ␤ aaccordingly to the age groups. Note that we divide by 365.25 because themortality regressions are at the day level, so dividing by 365.25 converts
this effect back in annual terms.
Our estimates suggest that the total number of affected migrant deathsis ⫺2,992.0 for females and ⫺1,598.9 for males, so that on the net(mobility-induced) differential exposure to cold temperature shocks delayed mortality of about 4,600 migrants.
Again, there is important variation across states in both the sign andmagnitude of the affected number of migrant deaths. At the two extremes
are California and Michigan. In conjunction with differential exposure tocold weather days, mobility to California delayed the mortality of 1,539
individuals in 2000, while mobility to Michigan accelerated the mortalityof 132 individuals.

681

where D̂ a is the counterfactual number of deaths at age a. For the agegroups for which mobility decreases longevity (positive ␤ a ), the counterfactual age of death is simply the given age. For the age groups for which
mobility increases longevity (negative ␤ a ), the counterfactual age of death
is obtained from the conditional probabilities of death.
To obtain the counterfactual share of death at age a, we simply divideD̂ a by the total number of deaths in the counterfactual distribution:F̂ a ⫽

冘

D̂ a
100
a⫽0

D̂ a ⫹ NA aa ⫽ 0, 1, . . . , 100,

where NA aj is defined as NA aj ⫽ D aj ⫺ A aj . The mean effect on longevity
is computed as follows:

冘
100

C.

Step 3: Counterfactual Distribution of Longevity, with Implied
Effect on Average of Death⫽

共F̂ a ⫺ F a 兲 ⫻ a.

a⫽0

We implement this by calculating the actual share of death at age a (F a ;see step 1) and the counterfactual share of death at age a, F̂ a . The averageage of death in the affected group of migrants is changed by mobility. Thisin turn changes the average age of death in the population as a whole.
Depending on the age group, mobility may accelerate death (positive ␤ a )or delay mortality (negative ␤ a ). The counterfactual age-of-death distribution is obtained as follows:冘冘
49

D̂ a ⫽

99j⫽1 m⫽a⫹1

关1共␤ a ⬍ 0兲 ⫻ P a兩m ⫻ A aj ⫹ 1共␤ a ⬎ 0兲 ⫻ a兴,

Based on our estimates, this number is 0.014 year for females and 0.008
year for males, or three to five days. To put this number in perspective, we
compare it to the annualized increase in longevity in the United Statesover the period 1970–2000. In annual terms, the average age of death in
the white population has increased by 0.20 to 0.25 years per calendar year
over the past thirty years. Assuming that the age distribution of movers
across states is constant over time, we can compare our estimatedlongevity effect of mobility to the annualized increased in overall longevity in the United States between 1970 and 2000. Our estimate of thelongevity effect of mobility corresponds to approximately 4%–7% of
these annual gains in overall longevity. We view this as a remarkably large
effect.