Faculty of Economics 
CAMBRIDGE WORKING PAPERS IN ECONOMICS 
JANEWAY INSTITUTE WORKING PAPERS   
Climate Change and Economic Activity: 
Evidence from U.S. States 
 
Kamiar 
Mohaddes 
University of 
Cambridge 
 
Ryan  
N. C. Ng 
University of 
Cambridge 
M. Hashem 
Pesaran 
University of 
Southern 
California 
Mehdi 
Raissi 
IMF 
Washington DC 
 
Jui-Chung 
Yang 
National 
Taiwan 
University 
 
Abstract 
We investigate the long-term macroeconomic effects of climate change across 48 U.S. states over the 
period 1963-2016 using a novel econometric strategy which links deviations of temperature and 
precipitation (weather) from their long-term moving-average historical norms (climate) to various 
state-specific economic performance indicators at the aggregate and sectoral levels. We show that 
climate change has a long-lasting adverse impact on real output in various states and economic sectors, 
and on labour productivity and employment in the United States. Moreover, in contrast to most cross-
country results, our within U.S. estimates tend to be asymmetrical with respect to deviations of climate 
variables (including precipitation) from their historical norms. 
 
Reference Details 
2205  Cambridge Working Papers in Economics 
2022/03 Janeway Institute Working Paper Series 
 
Published 21 January 2022 
 
Key Words Climate change, economic growth, adaptation, United States 
JEL-codes C33, O40, O44, O51, Q51, Q54 
 
Websites www.econ.cam.ac.uk/cwpe 
  www.janeway.econ.cam.ac.uk/working-papers  
Climate Change and Economic Activity:
Evidence from U.S. States
Kamiar Mohaddesay, Ryan N. C. Ngb, M. Hashem Pesaranc,
Mehdi Raissid and Jui-Chung Yange
a Judge Business School and King’s College, University of Cambridge, UK
b Faculty of Economics, University of Cambridge, UK
c Department of Economics, University of Southern California, USA
and Trinity College, University of Cambridge, UK
d International Monetary Fund, Washington DC, USA
e Department of Economics, National Taiwan University, Taiwan
January 20, 2022
Abstract
We investigate the long-term macroeconomic e¤ects of climate change across 48 U.S.
states over the period 1963–2016 using a novel econometric strategy which links devia-
tions of temperature and precipitation (weather) from their long-term moving-average
historical norms (climate) to various state-speci…c economic performance indicators
at the aggregate and sectoral levels. We show that climate change has a long-lasting
adverse impact on real output in various states and economic sectors, and on labour
productivity and employment in the United States. Moreover, in contrast to most
cross-country results, our within U.S. estimates tend to be asymmetrical with respect
to deviations of climate variables (including precipitation) from their historical norms.
JEL Classi…cations: C33, O40, O44, O51, Q51, Q54.
Keywords: Climate change, economic growth, adaptation, United States.
We are grateful to Tiago Cavalcanti, Francis X. Diebold, Christopher Hajzler, Stephane Hallegatte,
Zeina Hasna, John Hassler, Matthew E. Kahn, Per Krusell, Miguel Molico, Peter Phillips, Margit Reischer,
Ron Smith, Richard Tol, Carolyn A. Wilkins and seminar participants at the International Monetary Fund
(IMF), Bank of Lithuania, Bank of Canada, EPRG, Cambridge Judge Business School, the ERF 24th An-
nual Conference, the 2018 MIT CEEPR Research Workshop, the 2019 Keynes Fund Research Day, National
Institute of Economic and Social Research, Copenhagen Business School, Bank of England, Federal Reserve
Bank of San Francisco, London School of Economics, European Central Bank, and RES 2021 Annual Con-
ference for helpful comments and suggestions. We gratefully acknowledge …nancial support from the Keynes
Fund. The views expressed in this paper are those of the authors and do not necessarily represent those of
the IMF or its policy.
yCorresponding author. Email address: km418@cam.ac.uk.
1 Introduction
Kahn et al. (2021a) show that changes in the distribution of weather patterns (i.e., climate
change1) are not only a¤ecting low-income countries and those in hot climates, but also
advanced economies and those in cold climates (albeit to di¤erent degrees across climates
and income levels).2 Using estimates from a panel of 174 countries over the past half cen-
tury, they conduct a counterfactual study and show that in the absence of global mitigation
policies, per-capita GDP of the United States would be 10–17 percent lower by 2100.3 Do
these cross-country results hold in a within-country context (e.g., in the United States as
an advanced economy with a diverse climate and partial resilience-building success against
climate change)? How large are the e¤ects of climate change on state-level economic activity
in the U.S.? Are there level or growth e¤ects? Are the e¤ects non-linear and/or asymmet-
rical? What are the channels of impact and which sectors of the U.S. economy are a¤ected
the most? What is the role of climate variability and adaptation? Answers to the these
questions can inform the development of a long-term mitigation and adaptation strategy for
the United States (and by extension climate policies in other advanced economies).
While cross-country studies are informative, they also have drawbacks. Averaging tem-
perature and precipitation data at the country level leads to a loss of information, especially
in geographically diverse countries such as the United States. Using within-country data
for the U.S. and a novel econometric strategy, which links deviations of temperature and
precipitation (weather) from their long-term moving-average historical norms (climate) to
various state-speci…c economic performance indicators at the aggregate and sectoral levels,
we investigate the long-term macroeconomic e¤ects of weather patterns transformed by cli-
mate change across 48 states over the period 1963–2016. The within-country geographic
heterogeneity of the U.S. enables one to compare whether economic activity in ‘hot’or ‘wet’
states responds to a temperature increase in the same way as economic activity does in ‘cold’
or ‘dry’states. The richness of the United States data also allows for a more disaggregated
study of the climate change–growth relationship and enables one to test whether the country
at the aggregate level, parts of the country, or particular sectors of the economy have been
a¤ected more by climate change. It also allows one to investigate the channels of impact:
1Weather refers to atmospheric conditions over short periods of time (e.g., temperature and precipitation).
Climate refers to the long-term average and variability of weather. Climate change is a shift "in the state of
the climate that can be identi…ed (e.g., via statistical tests) by changes in the mean and/or the variability
of its properties, and that persists for an extended period, typically decades or longer”(IPCC (2014)).
2This …nding is in contrast with most papers in the literature— arguing that climate change has a limited
impact on economic activity of advanced countries as they are located in temperate places (or even bene…cial
e¤ects in cold climates); see, for instance, Burke et al. (2015), Dell et al. (2012), and Kalkuhl and Wenz
2020. An exception is a recent paper by Burke and Tanutama (2019).
3The upper bound of these losses allow for temperature increases to a¤ect the variability of temperature
shocks commensurately. Accounting for transition risks (in addition to physical risks) would lead to larger
losses (especially for advanced economies, see, for instance, Klusak et al. 2021 and Agarwala et al. 2021).
1
labour productivity, employment, and output growth in various sectors of the economy.4
Additionally, we contribute to the literature along the following dimensions. Firstly, we
di¤erentiate between level and growth e¤ects and estimate the long-term macroeconomic
impact of persistent increases in temperature and precipitation. Secondly, we use the half-
panel Jackknife FE (HPJ-FE) estimator proposed in Chudik et al. (2018) to deal with the
possible bias and size distortion of the commonly-used FE estimator. Thirdly, we depart
from the literature in focusing on changes in the distribution of weather patterns (not only
averages of temperature and precipitation but also their variability) and introducing an
implicit model of adaptation. Fourthly, we allow for state-speci…c and time-varying climate
thresholds— a subtle form of nonlinearity5— and also test for asymmetric weather e¤ects.
Finally, we avoid the econometric pitfalls associated with the use of trended variables, such
as temperature, in output growth equations (see Kahn et al. 2021b for details).
Our within-country results provide evidence for the damage that climate change causes
in the U.S. using various economic indicators at the state level: growth rates of Gross State
Product (GSP), GSP per capita, labour productivity, and employment as well as output
in di¤erent sectors (e.g., agriculture, manufacturing, services, retail and wholesale trade).
We show that if temperature increases by 0:01C annually above its historical norm across
U.S. states persistently, average per-capita real GSP growth will be lower by around 0:03
percentage points per year— a number that is smaller than those obtained in cross-country
regressions of Kahn et al. (2021a). We show that the impact of climate change on sectoral
output growth is broad based— each of the 10 sectors considered is a¤ected by at least one
of the four climate variables. Moreover, in contrast to most cross-country results, the within
U.S. estimates tend to be asymmetrical with respect to deviations of climate variables from
their historical norms (in the positive and negative directions). Finally, while we acknowledge
some resilience-building e¤orts in di¤erent states, the evidence seems to suggest that it has
not entirely o¤set the negative e¤ects of climate change at the country level.
The remainder of this paper is organized as follows. Section 2 presents the empirical
results and Section 3 concludes. Appendix A lists the data sources and their compilation.
4There are likely economic spillovers across states in the form of migration by households and pro…t
shifting by …rms to arbitrage di¤erences in cross-state regulatory regimes (tax systems and environmental
standards) as well as varying degrees of climate risks, which are important for a model’s dynamics. See, for
instance, Aquaro et al. (2021) for an example of how heterogeneous spillover e¤ects across regions can be
investigated. However, this paper abstracts from such spillovers given its focus on the long-term (equilibrium)
macroeconomic e¤ects of climate change.
5Non-linearity arises because growth is only a¤ected when temperature (or precipitation) goes above or
below a time-varying and state-speci…c historical threshold (i.e., the norm). It is due to this feature that
future growth is a¤ected not only by warming (or cooling if that was the case) but also by its variability.
2
2 Long-Run Impact of Climate Change on U.S. Eco-
nomic Growth
We …rst examine whether temperature across the 48 U.S. states has been increasing between
1963 and 2016. To this end, allowing for the signi…cant heterogeneity that exists across states
with respect to changes in temperature over time, we estimate state-speci…c regressions
Tit = aT i + bT it+ vT i;t; for i = 1; 2; :::; N = 48; (1)
where Tit denotes the weighted average temperature of state i at year t. The per annum av-
erage increase in land temperature for state i is given by bT i; with the corresponding country
measure de…ned by bT = N1Ni=1bT i. Our results suggest that, on average, temperature
in the 48 U.S. states has risen by 0:026 degrees Celsius (C) per year over 1963–2016 (i.e.,
b^T = 0:0260 (0:0007); with the standard error in brackets), with this trend estimate being
statistically signi…cant at the 1% level. All states experienced statistically signi…cant in-
creases in temperature over time (see Table 1). But, the 48 U.S. states as a whole underwent
more warming than the world on average. The U.S. average per annum temperature increase
of 0:026 was appreciably higher than the world average rise of 0:018 per annum, which is
close to that for Oklahoma, the state which saw the lowest average increase in temperature.
Table 1: Individual U.S. State Estimates of the Average Yearly Rise in Temper-
ature Over the Period 1963–2016
State b^;i State b^;i State b^;i
Alabama 0.0212z Maine 0.0288z Ohio 0.0263z
Arizona 0.0318z Maryland 0.0299z Oklahoma 0.0171z
Arkansas 0.0181z Massachusetts 0.0311z Oregon 0.0198z
California 0.0270z Michigan 0.0285z Pennsylvania 0.0280z
Colorado 0.0271z Minnesota 0.0320z Rhode Island 0.0320z
Connecticut 0.0316z Mississippi 0.0205z South Carolina 0.0250z
Delaware 0.0355z Missouri 0.0179z South Dakota 0.0234z
Florida 0.0228z Montana 0.0292z Tennessee 0.0234z
Georgia 0.0228z Nebraska 0.0222z Texas 0.0245z
Idaho 0.0245z Nevada 0.0273z Utah 0.0291z
Illinois 0.0223z New Hampshire 0.0299z Vermont 0.0318z
Indiana 0.0236z New Jersey 0.0343z Virginia 0.0266z
Iowa 0.0198z New Mexico 0.0300z Washington 0.0186z
Kansas 0.0186z New York 0.0308z West Virginia 0.0268z
Kentucky 0.0250z North Carolina 0.0257z Wisconsin 0.0307z
Louisiana 0.0210z North Dakota 0.0263z Wyoming 0.0279z
Notes: bbT i are the individual state-level estimates based on Tit = aT i + bT it + vT ;it, where Tit denotes the
average temperature (C) in state i in year t. z indicates statistical signi…cance at the 1% level.
We next examine the long-run impact of climate change on aggregate state-level economic
3
activity as well as states’ sectoral outputs. Such a within-country study is scant in the
literature as priority is given to studying the impact of climate change on a particular
sector of an economy (e.g., agricultural output) or to cross-country analyses. Guided by
the theoretical growth model with weather and climate variables developed in Kahn et al.
(2021a), we estimate the following panel ARDL model using the half-panel Jackknife FE
(HPJ-FE) estimator of Chudik et al. (2018):

yi;t = ai +
pX
l=1
 l
yi;tl +
pX
l=0
0l
~xi;tl + i;t; (2)
where yit is the log of real GSP of state i in year t or real GSP per capita, ~xit(m) = [ ~Tit (m)
+,
~Tit (m)
 ; ~Pit (m)
+ ; ~Pit (m)
]0, ~Tit (m) =

Tit  T i;t1(m)

and ~Pit (m) =

Pit  P i;t1(m)

are measures of temperature and precipitation relative to their historical norms, Tit and
Pit are the annual average temperature and precipitation of state i in year t, respectively,
and T i;t1(m) =
1
m
Pm
`=1 Ti;t` and P

i;t1(m) =
1
m
Pm
`=1 Pi;t` are the time-varying historical
norms of temperature and precipitation of state i over the preceding m years in each t.
Climate norms are typically computed as 30-year moving averages (Arguez et al. 2012 and
Vose et al. 2014), but to check the robustness of our results, we also consider historical
norms with m = 20 and 40.6 With ~Tit (m) and ~Pit (m) separated into positive and negative
values, we account for potential asymmetrical e¤ects of climate change on growth around
the threshold. The (average) long-run e¤ects, i , are calculated from the OLS estimates of
the short-run coe¢ cients in equation (2):  = 1
Pp
`=0 `, where  = 1
Pp
`=1 '`.
Since temperature is trended across the sample of 48 U.S. states, its inclusion in the
regression will introduce a linear trend in per capita output growth which is not supported
by the data, and can lead to biased estimates. This is the reason for specifying ARDL growth
regressions in deviations form (i.e., temperature and precipitation relative to their long-term
moving average historical norms), rather than in levels and/or squares of climate variables.7
Other important econometric considerations behind the use of ARDL regressions are set out
in Pesaran and Smith (1995), Pesaran (1997), and Pesaran and Shin (1999) who show that
the traditional ARDL approach can be used for long-run analysis; it is valid regardless of
whether the underlying variables are I (0) or I (1); and it is robust to omitted variables and
bi-directional feedback e¤ects between economic growth and its determinants. These features
of the panel ARDL approach are clearly appealing in our empirical application. For validity
of this technique, however, the dynamic speci…cation of the model needs to be augmented
with a su¢ cient number of lags so that regressors become weakly exogenous.8 Since we
6m = 30 corresponds to the o¢ cial World Meteorological Organization de…nition of climate (i.e., norm).
7For a detailed discussion see Kahn et al. (2021b), where it is shown that including Tit and T 2it in growth
regressions will introduce trends in 
yit.
8See Chudik et al. (2013), Chudik et al. (2016), and Chudik et al. (2017) for details.
4
are interested in studying the growth e¤ects of climate change (a long-term phenomenon),
the lag order should be long enough, and as such we set p = 4 for all the variables/states.
Using the same lag order across all the variables and states avoids data mining that could
accompany the use of state and variable speci…c lag order selection procedures such as Akaike
or Schwarz criteria. Note also that our primary focus here is on the long-run estimates rather
than the speci…c dynamics that might be relevant for a particular U.S. state.
Table 2 reports the long-run estimates of weather shocks on growth rates of real GSP
and real GSP per capita for 48 U.S. states over the period 1963–2016. We construct the
climate variables with historical norms computed using 20, 30, and 40 years moving-averages,
but consider the estimates based on the 30-year moving averages as our central estimates.
We observe that the estimated long-run coe¢ cients b
 ~Tit(m) ; b
 ~Pit(m)+, and b
 ~Pit(m) are
negative and statistically signi…cant in all cases except for one. Climate change a¤ects the
U.S. ecosystem not only through increases in average temperatures, but also through changes
in the extremes— more intense droughts; heavier snow and rainfall; as well as extreme cold.
However, b
 ~Tit(m)+ is not statistically signi…cant in three out of six speci…cations. While this
…nding might be explained by the improving resilience of the U.S. economy to increasing
temperature brought by climate change,9 the evidence for excessive temperature not a¤ecting
the U.S. economy is not conclusive as we will explain below.
While in their cross-country analysis, Kahn et al. (2021a) did not …nd any statistically
signi…cant impact from deviations of precipitation from its historical norms on output growth,
in our within-country study of the United States, we …nd that deviations of precipitation
above and below its historical norm a¤ect various measures of state-level economic activity
and these estimates are statistically signi…cant.10 This is because averaging precipitation at
the country level leads to a loss of information, especially in geographically diverse countries
with varied precipitation patterns. While the national average precipitation may be close
to its historical norm, there is signi…cant heterogeneity across states with some experiencing
plenty of rain and snow and others, like California, su¤ering from drought for many years.
By conducting a within-country study, we account for the variation of precipitation across
the states, which is important and does indeed a¤ect economic activity (Table 2).
Considering the richness of our U.S. database, which includes data on state-level em-
ployment from 1976, we can also examine the long-run impact of climate change on labour
productivity and employment growth directly, in addition to re-estimating the regressions
over the period 1976–2016. We, therefore, re-estimate the model for an extended set of
outcome variables, with yit being the natural logarithm of: (i) real GSP, (ii) real GSP
per capita, (iii) real GSP per employed (measuring labour productivity), or (iv) employ-
9For example, currently about 90 percent of American households have air conditioning.
10The importance of focusing on deviations of climate variables from their historical norms is also high-
lighted by recent research which demonstrate that di¤erent regions of the United States have acclimated
themselves to their own temperature niche; see, for instance, Heutel et al. (2016).
5
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n
g-
ru
n
e¤
ec
ts
,

i
,
ar
e
ca
lc
u
la
te
d
fr
om
th
e
O
L
S
es
ti
m
at
es
of
th
e
sh
or
t-
ru
n
co
e¢
ci
en
ts
in
eq
u
at
io
n
(2
):

=


1
P p `=
0

`
,
w
h
er
e

=
1

P p `=
1
'
`
.
T
h
e
la
g
or
d
er
,
p
,
is
se
t
to
4.
S
ta
n
d
ar
d
er
ro
rs
in
p
ar
en
th
es
es
ar
e
es
ti
m
at
ed
by
th
e
es
ti
m
at
or
p
ro
p
os
ed
in
P
ro
p
os
it
io
n
4
of
C
hu
d
ik
et
al
.
(2
01
8)
.
A
st
er
is
ks
in
d
ic
at
e
st
at
is
ti
ca
l
si
gn
i…
ca
n
ce
at
1%
(*
**
),
5%
(*
*)
,
an
d
10
%
(*
)
le
ve
ls
.
6
ment, but over the period 1976 to 2016. These results are reported in Table 3. Across all
speci…cations, the estimated long-run coe¢ cients b
 ~Tit(m) and b
 ~Pit(m) are negative and
statistically signi…cant at the 1% level for almost all outcome variables. Therefore, when
temperature and precipitation fall below their historical norms, state-level economic activity
su¤ers, employment declines, and labour productivity growth falls (for b
 ~Tit(m)).
While in Table 2, the climate variable ~Tit (m)
+, did not have a statistically signi…cant
impact on state-level output growth in three out of six speci…cations (over the period 1963–
2016), the results change substantially when we consider the 1976–2016 sub-sample in Table
3. Consistent with cross-country estimates, b
 ~Tit(m)+ is now negative and statistically sig-
ni…cant for various speci…cations and dependent variables: real GSP, real GSP per capita,
real GSP per employed, and employment. The size of the estimates for b
 ~Tit(m)+ is smaller
in absolute value than those obtained in cross-country regressions of Kahn et al. (2021a),
partly re‡ecting a higher degree of adaptation in the U.S. to climate change. Nonetheless,
contrary to most studies in the literature, our estimates are not negligible. Our results are
supported by Deryugina and Hsiang (2014) and Behrer and Park (2017), who exploit county-
level variations in climate variables over time in the U.S. and …nd that hotter temperatures
damage economic activity, and also by Colacito et al. (2019) who …nd that an increase in
summer temperatures has adverse e¤ects on GSP growth in the United States.
2.1 Adaptation and the U.S. Economy
While there is growing evidence of the bene…ts of climate-change adaptation at the sectoral
and micro level, the macroeconometric-climate literature does not provide conclusive esti-
mates of the economic bene…ts of adaptation. An exception is Kahn et al. (2021a) who …nd
that adaptation has the potential to halve the long-term growth e¤ects of global warming.
They model adaptation by assuming di¤erent speeds of the formation of historical norms.
Their results hold in our within-country study of the U.S. states. Another way to assess
adaptation is to test how the elasticity of growth to climate variables evolves over time.
Speci…cally, if the U.S. economy were adapting to climate change, ceteris paribus, should we
not expect the impact of deviations of temperature and precipitation from their historical
norms to be shrinking over time? To investigate this hypothesis, we re-estimate the model
over di¤erent time windows using real GSP per capita growth as the dependent variable. We
start with the full sample, 1963–2016, and then drop a year at a time (with the last estima-
tion being carried out for the sub-sample 1983–2016). The results are plotted in Figure 1,
showing that the estimated coe¢ cients are becoming larger (in absolute value) over time.
Do these results cast doubt on the e¢ cacy of adaptation e¤orts in the United States over
the last …ve decades? Probably not. Ceteris paribus, while it is expected that adaptation
weakens the relationship between climate change and economic growth over time, we cannot
7
T
ab
le
3:
L
on
g-
R
u
n
E
¤
ec
ts
of
C
li
m
at
e
C
h
an
ge
on
th
e
G
ro
w
th
R
at
e
of
M
a
jo
r
E
co
n
om
ic
In
d
ic
at
or
s
fo
r
th
e
U
n
it
ed
S
ta
te
s,
19
76
–2
01
6
H
is
to
ri
ca
l
N
o
rm
:
2
0
Y
ea
r
M
A
3
0
Y
ea
r
M
A
4
0
Y
ea
r
M
A
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l
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ea
l
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ea
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p
lo
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en
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ea
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ea
l
G
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m
p
lo
ym
en
t
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ea
l
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S
P
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ea
l
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S
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E
m
p
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ym
en
t
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ap
it
a
p
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p
lo
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d
p
er
C
ap
it
a
p
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m
p
lo
ye
d
p
er
C
ap
it
a
p
er
E
m
p
lo
ye
d
b  
~ T i
t
(m
)+
-0
.0
39
1*
**
-0
.0
27
9*
**
-0
.0
18
8*
**
-0
.0
13
6*
*
-0
.0
37
9*
**
-0
.0
27
3*
**
-0
.0
19
0*
**
-0
.0
13
5*
*
-0
.0
37
1*
**
-0
.0
27
1*
**
-0
.0
18
8*
**
-0
.0
12
5*
*
(0
.0
10
8)
(0
.0
08
4)
(0
.0
06
7)
(0
.0
06
5)
(0
.0
10
7)
(0
.0
08
3)
(0
.0
06
7)
(0
.0
06
2)
(0
.0
10
5)
(0
.0
08
2)
(0
.0
06
5)
(0
.0
06
2)
b  
~ T i
t
(m
)
-0
.1
55
4*
**
-0
.1
19
9*
**
-0
.0
60
4*
**
-0
.0
72
3*
**
-0
.1
95
1*
**
-0
.1
50
5*
**
-0
.0
77
9*
**
-0
.0
95
4*
**
-0
.2
08
9*
**
-0
.1
61
6*
**
-0
.0
84
7*
**
-0
.1
02
7*
**
(0
.0
31
5)
(0
.0
23
3)
(0
.0
16
6)
(0
.0
18
3)
(0
.0
35
8)
(0
.0
25
9)
(0
.0
19
1)
(0
.0
20
5)
(0
.0
38
8)
(0
.0
28
3)
(0
.0
20
7)
(0
.0
22
1)
b  
~ P
i
t
(m
)+
-0
.1
11
2*
-0
.0
93
0*
-0
.0
38
1
-0
.0
43
4
-0
.1
59
8*
*
-0
.1
34
5*
**
-0
.0
44
3
-0
.0
85
3*
*
-0
.1
32
6*
*
-0
.1
12
0*
*
-0
.0
29
9
-0
.0
79
2*
*
(0
.0
63
0)
(0
.0
50
0)
(0
.0
39
0)
(0
.0
40
9)
(0
.0
63
4)
(0
.0
49
8)
(0
.0
39
5)
(0
.0
39
5)
(0
.0
61
3)
(0
.0
48
4)
(0
.0
38
5)
(0
.0
38
7)
b  
~ P
i
t
(m
)
-0
.2
84
0*
**
-0
.1
98
1*
**
-0
.0
51
0
-0
.2
08
2*
**
-0
.3
40
7*
**
-0
.2
41
2*
**
-0
.0
55
3
-0
.2
51
5*
**
-0
.3
51
5*
**
-0
.2
50
3*
**
-0
.0
53
0
-0
.2
76
3*
**
(0
.0
78
1)
(0
.0
60
0)
(0
.0
44
3)
(0
.0
55
3)
(0
.0
83
7)
(0
.0
62
8)
(0
.0
46
7)
(0
.0
59
4)
(0
.0
87
8)
(0
.0
66
5)
(0
.0
49
0)
(0
.0
63
6)
b 
0.
50
92
**
*
0.
63
27
**
*
0.
72
19
**
*
0.
43
71
**
*
0.
50
16
**
*
0.
63
01
**
*
0.
70
35
**
*
0.
43
82
**
*
0.
49
75
**
*
0.
62
15
**
*
0.
69
42
**
*
0.
42
90
**
*
(0
.0
50
3)
(0
.0
52
9)
(0
.0
75
6)
(0
.0
44
5)
(0
.0
50
4)
(0
.0
53
0)
(0
.0
75
6)
(0
.0
44
4)
(0
.0
50
2)
(0
.0
53
0)
(0
.0
74
9)
(0
.0
44
5)
N
o.
of
st
at
es
(N
)
48
48
48
48
48
48
48
48
48
48
48
48
T
36
36
36
36
36
36
36
36
36
36
36
36
N

T
17
28
17
28
17
28
17
28
17
28
17
28
17
28
17
28
17
28
17
28
17
28
17
28
N
ot
es
:
T
h
e
H
P
J-
F
E
es
ti
m
at
es
ar
e
b
as
ed
on
th
e
fo
ll
ow
in
g
sp
ec
i…
ca
ti
on


y
i;
t
=
a
i
+
P p l=
1
 
l

y
i;
t
l
+
P p l=
0

0 l

~x
i;
t
l
+
 i
;t
,
w
h
er
e
y
it
is
th
e
lo
g
of
(i
)
re
al
G
S
P
,
(i
i)
re
al
G
S
P
p
er
ca
p
it
a,
(i
ii
)
re
al
G
S
P
p
er
em
p
lo
ye
d
,
an
d
(i
v
)
em
p
lo
ym
en
t
of
st
at
e
i
in
ye
ar
t,
~x
it
(m
)
=
[
~ T
it
(m
)+
,
~ T
it
(m
)
;
~ P
it
(m
)+
;
~ P
it
(m
)
]0 ,
~ T
it
(m
)
=
h T it

T
 i;t

1
(m
)i an
d
~ P
it
(m
)
=
h P it

P
 i;t

1
(m
)i
ar
e
m
ea
su
re
s
of
te
m
p
er
at
u
re
an
d
p
re
ci
p
it
at
io
n
re
la
ti
ve
to
th
ei
r
h
is
to
ri
ca
l
n
or
m
s,
T
it
an
d
P
it
ar
e
th
e
an
nu
al
av
er
ag
e
te
m
p
er
at
u
re
(i
n
C
el
si
u
s)
an
d
p
re
ci
p
it
at
io
n
(i
n
m
et
re
s)
of
st
at
e
i
in
ye
ar
t,
re
sp
ec
ti
ve
ly
,
an
d
T
 i;t

1
(m
)
=
1 m
P m `=
1
T
i;
t
`
an
d
P
 i;t

1
(m
)
=
1 m
P m `=
1
P
i;
t
`
ar
e
th
e
ti
m
e-
va
ry
in
g
h
is
to
ri
ca
l
n
or
m
s
of
te
m
p
er
at
u
re
an
d
p
re
ci
p
it
at
io
n
of
st
at
e
i
ov
er
th
e
p
re
ce
d
in
g
m
ye
ar
s
in
ea
ch
t.
T
h
e
lo
n
g-
ru
n
e¤
ec
ts
,

i
,
ar
e
ca
lc
u
la
te
d
fr
om
th
e
O
L
S
es
ti
m
at
es
of
th
e
sh
or
t-
ru
n
co
e¢
ci
en
ts
in
eq
u
at
io
n
(2
):

=


1
P p `=
0

`
,
w
h
er
e

=
1

P p `=
1
'
`
.
T
h
e
la
g
or
d
er
,
p
,
is
se
t
to
4.
S
ta
n
d
ar
d
er
ro
rs
in
p
ar
en
th
es
es
ar
e
es
ti
m
at
ed
by
th
e
es
ti
m
at
or
p
ro
p
os
ed
in
P
ro
p
os
it
io
n
4
of
C
hu
d
ik
et
al
.
(2
01
8)
.
A
st
er
is
ks
in
d
ic
at
e
st
at
is
ti
ca
l
si
gn
i…
ca
n
ce
at
1%
(*
**
),
5%
(*
*)
,
an
d
10
%
(*
)
le
ve
ls
.
8
Figure 1: Long-Run E¤ects of Climate Change on per capita Real GSP Growth
in the United States, 1963–2016
Notes: Figures show the long-run e¤ects (and their 95% standard error bands) of climate change on state-
level economic growth in the United States over di¤erent windows, using the ARDL speci…cation (2). We
start the estimation with the full sample (1963–2016) and then drop one year at a time, ending with the
…nal estimates based on the 1983–2016 sub-sample.
9
conclude that the U.S. economy has not been adapting to climate change based on Figure
1. First, adaptation e¤orts might be concentrated in certain sectors. Second, it may be the
case that adaptation is not keeping pace with climate change; i.e., global temperatures have
increased at an unprecedented pace over the past 40 years. Third, the e¤ects of adaptation
might have been o¤set by structural changes to the U.S. economy (that is a shift of value
added to sectors that are more exposed to climate change). Fourth, if …rms underestimate
the likelihood or severity of future weather events, they may not adapt su¢ ciently; i.e., adap-
tation technologies are readily available but the take-up is limited by …rms. In a survey of
private sector organizations across multiple industries within the Organization for Economic
Cooperation and Development (OECD) countries, Agrawala et al. (2011) …nd that only
few …rms have taken su¢ cient steps to assess and manage the risks from climate change.
Fifth, according to Deryugina and Hsiang (2014) …rms tend to under-invest in adaptation
owing to its high cost.11 We argue that there has been some adaptation in the U.S. given
that the estimates in Tables 2 and 3 are generally smaller than those obtained in the cross-
country study of Kahn et al. (2021a) and they are increasing with m (if expressed in per
annum terms)— i.e., the faster is the change in historical norms, the less is the size of income
losses across U.S. states. However, the evidence suggests that adaptation e¤orts should be
complemented with mitigation policies to minimize the adverse e¤ects of climate change.
2.2 Further Evidence from U.S. Sector Level Data
Adaptation and mitigation can occur in the short-term through a reallocation of resources,
and in the long-term through investment in research and development, innovation, or a shift
in the economic structure of the country towards an industry mix that is less vulnerable to
climate change. Given that adaptation is relatively easier and more e¤ective to implement in
some industries than others, we …rst need to assess which sectors/industries are more likely to
be a¤ected by climate change in the U.S. economy. Focusing on di¤erent sectors/industries
also helps shed light on the channels through which climate change a¤ects the United States
economy. We consider ten sectors, and due to lack of worker per sector data at the state
level, we only report the results for state-level output growth.12
The long-run sectoral e¤ects of climate change estimated on the panel of the 48 U.S.
states over the period 1963–2016 are reported in Table 4. The estimates show that the
impact is broad based— each of the 10 sectors is a¤ected by at least one of the four climate
variables. Speci…cally, the agricultural sector is negatively impacted by a rise in temperature
above its historical norm, b
 ~Tit(m)+ < 0. In addition, precipitation above and below the norm
also exert negative e¤ects on agricultural output growth. These results are in line with the
11For a discussion of costs associated with diverting funds away from productive capital, see Mohaddes and
Williams (2020). Other reasons for underinvestment include knowledge spillovers and networks externalities.
12See Appendix A for further details.
10
…ndings of Burke and Emerick (2016), who consider corn and soy farming in the U.S. over
the period 1955–2005, and …nd that, despite some adaptation e¤orts by farmers, agricultural
output is damaged by extreme heat and excessive precipitation. Note also that the cost of
adaptation to climate change is high in the agricultural sector— constructing greenhouses or
varying crop mixes involves heftier investments than …tting air conditioning units in o¢ ces.
Table 4 also illustrates that deviations of all four climate variables from their historical
norms have adverse e¤ects on output growth in the manufacturing sector. While the negative
impact of climate change on agricultural production is well studied, the adverse e¤ects on
the manufacturing sector in the United States are only being discussed in the new climate
economy literature (using micro-data analyses). For example, Cachon et al. (2012) use
weekly production data from 64 automobile plants in the U.S. and …nd that climate variations
(extensive periods of rain and snow, high heat, and severe winds), lead to costly production
volatility, and have adverse e¤ects on labour productivity, in line with our results. Moreover,
our estimates show that output growth in mining, construction, transport, retail trade,
wholesale trade, services and government sectors are all negatively a¤ected by unusually
cold days in the U.S. as consumer spending falls (households may delay shopping or even
cut from spending owing to higher heating costs or home-repair expenses); supply chains are
interrupted;13 and construction projects are delayed. See also Bloesch and Gourio (2015)
for further supporting evidence. Heavy rain can also reduce access to mountainous mining
regions, where large deposits are generally found, thereby reducing output growth in the
mining sector. Construction and transportation activities are also a¤ected by rain/snow.
Most discussions of climate change focus on the expected increase in average global
temperatures over the next century (i.e. global warming). However, the frequency and
severity of weather events (such as heat or cold waves, droughts and ‡oods, as well as
natural disasters) depend heavily on the variability of temperatures and precipitation as well
as their mean. The larger the swings, the more often extremely hot or cold and wet or dry
conditions can wreak havoc; see, for instance, Swain et al. 2018. Given current projections
of rising average global temperature over the next century, the likelihood that temperatures
persistently drift above their historical norm is very high. As we showed above, this could
lead to a permanent negative impact on state-level output growth (that is lower production
growth in all sectors of the United States economy apart from the mining, government, and
…nance, insurance and real estate sectors). While persistent deviations of precipitation from
its historical norm (either above and below) or below-the-norm temperatures are less likely,
the swings (variability) could be unprecedentedly large owing to climate change, and hence,
the negative impact on state-level output growth could be sizable and long lasting.
Overall, the industry-level results in Table 4 and the state-level results in Tables 2–
13For example, steel production along the coast of Lake Michigan was majorly disrupted during the brutal
2013-14 winter, because frozen Great Lakes meant that cargoes could not be moved via boats as usual.
11
T
ab
le
4:
L
on
g-
R
u
n
E
¤
ec
ts
of
C
li
m
at
e
C
h
an
ge
on
th
e
O
u
tp
u
t
G
ro
w
th
of
V
ar
io
u
s
S
ec
to
rs
in
th
e
U
n
it
ed
S
ta
te
s,
19
63
–2
01
6
A
gr
ic
u
lt
u
re
T
ra
n
sp
or
t
F
in
an
ce
F
or
es
tr
y
M
in
in
g
C
on
st
ru
ct
io
n
M
an
u
fa
ct
u
ri
n
g
C
om
m
u
n
ic
at
io
n
W
h
ol
es
al
e
T
ra
d
e
R
et
ai
l
T
ra
d
e
In
su
ra
n
ce
S
er
vi
ce
s
G
ov
er
n
m
en
t
F
is
h
er
ie
s
P
u
b
li
c
U
ti
li
ti
es
R
ea
l
E
st
at
e
b  
~ T i
t
(m
)+
-0
.0
32
3*
-0
.0
03
0
-0
.0
58
7*
**
-0
.0
66
6*
**
-0
.0
26
1*
*
-0
.0
62
8*
**
-0
.0
53
1*
**
0.
07
30
**
*
-0
.0
33
6*
**
0.
01
82
*
(0
.0
17
8)
(0
.0
40
0)
(0
.0
20
4)
(0
.0
16
3)
(0
.0
11
2)
(0
.0
20
9)
(0
.0
12
6)
(0
.0
16
1)
(0
.0
11
0)
(0
.0
10
7)
b  
~ T i
t
(m
)
-0
.0
18
4
-0
.1
88
7*
**
-0
.1
77
7*
**
-0
.1
13
6*
**
-0
.0
63
3*
**
-0
.2
36
5*
**
-0
.1
67
4*
**
0.
00
50
-0
.1
20
1*
**
-0
.0
44
6*
*
(0
.0
31
5)
(0
.0
72
8)
(0
.0
40
0)
(0
.0
27
0)
(0
.0
17
5)
(0
.0
38
9)
(0
.0
23
7)
(0
.0
30
9)
(0
.0
25
2)
(0
.0
19
9)
b  
~ P
i
t
(m
)+
-0
.3
05
4*
**
-0
.6
05
2*
**
-0
.2
16
4*
*
-0
.2
38
2*
**
0.
13
40
**
-0
.0
91
7
-0
.0
71
1
-0
.0
24
2
-0
.1
18
2*
0.
00
00
(0
.0
82
6)
(0
.2
16
1)
(0
.1
06
9)
(0
.0
79
2)
(0
.0
54
7)
(0
.1
11
5)
(0
.0
66
0)
(0
.0
80
3)
(0
.0
63
5)
(0
.0
55
5)
b  
~ P
i
t
(m
)
-0
.5
49
9*
**
-0
.2
97
3
-0
.2
00
0
-0
.2
68
9*
**
-0
.1
02
2
-0
.2
98
7*
*
-0
.2
39
0*
**
0.
04
62
-0
.1
31
7*
0.
01
84
(0
.1
18
8)
(0
.2
88
4)
(0
.1
34
4)
(0
.1
01
9)
(0
.0
68
5)
(0
.1
31
2)
(0
.0
84
6)
(0
.1
01
3)
(0
.0
77
2)
(0
.0
78
7)
b 
1.
88
92
**
*
0.
81
33
**
*
0.
64
12
**
*
0.
95
99
**
*
0.
85
36
**
*
0.
48
30
**
*
0.
57
87
**
*
0.
59
44
**
*
0.
41
35
**
*
0.
39
02
**
*
(0
.0
84
3)
(0
.0
45
9)
(0
.0
33
1)
(0
.0
72
6)
(0
.0
58
1)
(0
.0
39
4)
(0
.0
31
2)
(0
.0
54
6)
(0
.0
41
4)
(0
.0
45
8)
N
o.
of
st
at
es
(N
)
48
47
48
48
48
48
48
48
48
48
T
48
48
48
48
48
48
48
48
48
48
N

T
23
04
22
56
23
04
23
04
23
04
23
04
23
04
23
04
23
04
23
04
N
ot
es
:
T
h
e
H
P
J-
F
E
es
ti
m
at
es
ar
e
b
as
ed
on
th
e
fo
ll
ow
in
g
sp
ec
i…
ca
ti
on


y
i;
t
=
a
i
+
P p l=
1
 
l

y
i;
t
l
+
P p l=
0

0 l~x
i;
t
l
+
 i
;t
,
w
h
er
e
y
it
is
th
e
lo
g
of
se
ct
or
al
re
al
ou
tp
u
t
in
st
at
e
i
in
ye
ar
t,
~x
it
(m
)
=
[
~ T
it
(m
)+
,
~ T
it
(m
)
;
~ P
it
(m
)+
;
~ P
it
(m
)
]0 ,
~ T
it
(m
)
=
h T it

T
 i;t

1
(m
)i a
n
d
~ P
it
(m
)
=
h P it

P
 i;t

1
(m
)i a
re
m
ea
su
re
s
of
te
m
p
er
at
u
re
an
d
p
re
ci
p
it
at
io
n
re
la
ti
ve
to
th
ei
r
h
is
to
ri
ca
l
n
or
m
s,
T
it
an
d
P
it
ar
e
th
e
an
nu
al
av
er
ag
e
te
m
p
er
at
u
re
(i
n
C
el
si
u
s)
an
d
p
re
ci
p
it
at
io
n
(i
n
m
et
re
s)
of
st
at
e
i
in
ye
ar
t,
re
sp
ec
ti
ve
ly
,
an
d
T
 i;t

1
(m
)
=
1 m
P m `=
1
T
i;
t
`
an
d
P
 i;t

1
(m
)
=
1 m
P m `=
1
P
i;
t
`
ar
e
th
e
ti
m
e-
va
ry
in
g
h
is
to
ri
ca
l
n
or
m
s
of
te
m
p
er
at
u
re
an
d
p
re
ci
p
it
at
io
n
of
st
at
e
i
ov
er
th
e
p
re
ce
d
in
g
m
ye
ar
s
in
ea
ch
t
(w
it
h
m
=
3
0
in
th
is
ca
se
).
T
h
e
lo
n
g-
ru
n
e¤
ec
ts
,

i
,
ar
e
ca
lc
u
la
te
d
fr
om
th
e
O
L
S
es
ti
m
at
es
of
th
e
sh
or
t-
ru
n
co
e¢
ci
en
ts
in
eq
u
at
io
n
(2
):

=


1
P p `=
0

`
,
w
h
er
e

=
1

P p `=
1
'
`
.
T
h
e
la
g
or
d
er
,
p
,
is
se
t
to
4.
S
ta
n
d
ar
d
er
ro
rs
in
p
ar
en
th
es
es
ar
e
es
ti
m
at
ed
by
th
e
es
ti
m
at
or
p
ro
p
os
ed
in
P
ro
p
os
it
io
n
4
of
C
hu
d
ik
et
al
.
(2
01
8)
.
A
st
er
is
ks
in
d
ic
at
e
st
at
is
ti
ca
l
si
gn
i…
ca
n
ce
at
1%
(*
**
),
5%
(*
*)
an
d
10
%
(*
)
le
ve
ls
.
12
3, show that deviations of temperature below its historical norms in the U.S. as well as
deviations of precipitation from its historical norm are detrimental to long-run state-level
and industry-level output growth. When it comes to deviations of temperature above its
historical norms, the estimates are negative and statistically signi…cant at the aggregate
state-level (in the more recent sample) and for all economic sectors apart from mining,
government, and …nance, insurance and real estate sectors. In fact b
 ~Tit(m)+ is positive and
statistically signi…cant for government services (at the 10% level) and …nance, insurance
and real estate sectors, but most likely this re‡ects government spending on relief measures
and higher insurance premiums in response to climate change.
We acknowledge some resilience building activities in advanced economies, but the ev-
idence from our U.S. within-country study seems to suggest that while adaptation might
have reduced the negative e¤ects in certain sectors, it has not completely o¤set them at the
macro level (see Table 3 and Figure 1). Behrer and Park (2017) note that even the most
well-adapted regions in the United States su¤er negative production e¤ects from hotter tem-
peratures and Colacito et al. (2019) show that an increase in average summer temperatures
will have negative e¤ects on nominal output in various sectors, such as agriculture, construc-
tion, retail, services, and wholesale trade.
3 Concluding Remarks
Using data on 48 U.S. states from 1963 to 2016, and a novel econometric strategy (that
di¤erentiates between level and growth e¤ects including in the long term; accounts for bi-
directional feedbacks between growth and climate change; considers asymmetric weather
e¤ects; allows for nonlinearity and an implicit model of adaptation; and deals with temper-
ature being trended), we provided evidence for the damage that climate change causes in
the U.S. using GSP, GSP per capita, labour productivity, and employment as well as output
growth in ten economic sectors (such as agriculture, construction, manufacturing, services,
retail and wholesale trade). While certain sectors in the U.S. economy might have adapted to
higher temperatures, economic activity in the U.S. overall and at the sectoral level continues
to be sensitive to deviations of temperature and precipitation from their historical norms.
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15
A Data Appendix
We obtain state-level economic activity data from the Bureau of Economic Analysis (BEA). Real
Gross State Product (GSP) data is available from 1977, but nominal GSP data is available from
1963. We de‡ate the nominal GSP series using the consumer price index (CPI) for each state, and
splice the resulting data over 1963–1977 with the real GSP from 1977 using annual growth rates,
to construct a real GSP series for 1963–2016.
BEA provides output by sector at the state level from 1963. However, there are two issues with
this database. Firstly, there was a change in industrial classi…cations in 1997: from 1963 to 1997,
the Standard Industrial Classi…cation (SIC) consists of ten divisions, while from 1997 onwards,
the North American Industry Classi…cation System (NAICS) gradually replaces the SIC, further
branching the ten divisions into …fteen sectors.14 Secondly, as with the GSP data, only nominal
sectoral output data (by SIC divisions) is available before 1977. Real sectoral output is available
in both SIC and NAICS classi…cation in 1997. This allows us to construct the real sectoral output
series from 1963–2016. Speci…cally, building a series over the period 1963 to 2016 involves two steps:
(i) reconciling SIC and NAICS classi…cations (see Table A.1), and (ii) splicing the most recent real
series (1997–2016) backwards using growth rates from the de‡ated nominal series (1963–1997).
Table A.1: Division (SIC) and Sector (NAICS) Classi…cations
Division (SIC) Sector (NAICS)
Agriculture, Forestry, Fisheries Agriculture, Forestry, Fishing & Hunting
Mining Mining
Construction Construction
Manufacturing Manufacturing
Transportation & Warehousing
Transport, Communication, and Public Utilities Information
Utilities
Wholesale Trade Wholesale Trade
Retail Trade Retail Trade
Finance, Insurance, and Real Estate Finance/Insurance/Real Estate/Rental/Leasing
Professional & Business Services
Services Educational Services/Health Care/Social Assistance
Arts/Entertainment/Recreation/Accommodation/Food Services
Other Services, Ex Government
Government Government
We use BEA’s producer price index (PPI) data to de‡ate the nominal industry outputs under
SIC for the years 1963–1976. As the PPI data is constructed based on NAICS, we use the SIC-
NAICS matching in Table A.1 for the PPI de‡ator. Where there is more than one NAICS sector
matched to a SIC division, we take a simple arithmetic average of the PPI of all matched NAICS
sectors. From 1997 onwards, real output by sector is available based on NAICS classi…cation.
We, therefore, aggregate the NAICS real output by industry to SIC divisions using our matching
scheme, and splice these series backwards using the growth rates of real sectoral output under SIC
in 1963–1997. This gives us real output by sector and state for the period 1963 to 2016.15
14See Kort (2001) for more details.
15Note that "Agriculture, Forestry, Fishing & Hunting" and "Mining" data is not available for Rhode
16
We collect monthly state-level, area-weighted climate data from the NOAA’s National Centres
for Environmental Information (NCEI). The NCEI reports monthly average temperature and pre-
cipitation16 for each state from aggregates of climate readings across weather stations, adjusting for
the distribution of stations and terrain. Temperature is measured in degrees Fahrenheit and pre-
cipitation in inches. We convert them into degrees Celsius and metres, respectively. The monthly
averages in each year within the sample period are then used to obtain annual averages.
Finally, we obtain U.S. employment data from the Bureau of Labor Statistics (BLS). We take
annual, state-level number of employed persons that encompasses "persons 16 years and over in
the civilian noninstitutional population" under a wide range of employment conditions.
Island in 2016 and agricultural data in 2016 is also unavailable for Delaware. Moreover, the mining industry
of Delaware is excluded from our sample due to multiple irregular missing entries.
16Snow is included as melted precipitation in rain gauges under NOAA methodology.
17