Climate Change and Outdoor Labor Markets:
the Rise of Dropouts of Adult Males∗
Masahiro Yoshida†
March 22, 2024

Abstract
Male labor force participation in developed economies has ubiquitously declined
since the 1970s. This paper tests whether modern global warming fueled dropouts ofprime-aged adult males by harming their traditional advantage of working outdoors.
Exploiting climate change variation across U.S. commuting zones, constructed from
granular weather station records during 1970-2019, I find that 10 more average hotdays (above 75F) per year hurt labor force participation rate (LFPR) of prime-aged
males by 0.28 percentage points. Overall, climate change accounted for 10-15% of thenationwide LFPR drop in the new century. I find that climate impact critically depends
on regional prevalence of outdoor jobs, which I compute from O*NET Work Context
Survey, and thus, relatively yield fewer impacts on females. The effect is higher on nonrainy and humid hot days; if regional demography is less-educated and middle-aged; if
an adoption rate of residential air conditioner is higher. I also find that climate change
hurt employment rate and wages of outdoor workers, with limited transfer to indoor
sector and increased unemployment. Collectively, the finding suggests that climatechange exacerbates socio-economic inequality.
JEL Classification: J22, J12, J13, Q54
Keywords: Climate change, labor force participation rate, outdoor jobs, air conditioner∗

I benefit from discussions with Jun Goto, Erika Igarashi, Tomoya Mori, Xincheng Qiu, Hiroya Saruya,
Haruka Takayama, Kensuke Teshima, Ken Yamada, Yajie Wang and Yuta Watabe. I appreciate feedbacks
from participants in workshop at GRIPS, Hitotsubashi, Keio, Kyoto (empirical), Kyoto (urban) and Waseda.
†
Department of Political Science and Economics, Waseda University, Tokyo
E-mail: m.yoshida@waseda.jp

1

1

Introduction

The globe has become and will be a hotter planet. Climate science established that an
unprecedented temperature rise for two millennia loomed up since the dawn of 19th century
industrialization and spiked around the 1970s.1 The warming further accelerated in the
21st century (Global Climate Report, NOAA), ended up being framed as global boiling(Guterres, 2023)2 . Traditionally, economists have explored the climate impact on agricultural
production (e.g., Deschênes and Greenstone (2007)). Shifting focus to human behaviors,
however, recent studies have uncovered climate impact on human psyche, exemplified bynegative tweets (Baylis (2020)), increased suicides (Burke et al. (2018)) and violent crimes(Ranson (2014)). Does climate change alter labor market attachment of males, consisting ofmajority of outdoor workers?
This paper proposes and tests a hypothesis that modern global warming accelerated in
the new century contributed to a global decline in labor market participation rate (LFPR
below) of prime-aged males in developed countries (See e.g., Grigoli, Koczan and Topalova(2020)). I feature the most serious country, the U.S.: until 1970, a dropout rate3 for U.S.
prime-aged (aged 25-54) males had been 2-4%, in 2019, however, the rate has consistently
risen to an alarming height of 12%4 , leading to rising income inequality, morbidity and poor
subjective well-being (Krueger (2017)). Only consensus in the long-standing literature isthat no single dominant factors explain for the drop (See Abraham and Kearney (2020) and
Binder and Bound (2019) for a comprehensive survey)5 .
To bridge climate and adult males’ labor market attachment, I draw on O*NET Work
Context Survey to document that approximately 30% of prime-aged males have worked
1

See e.g., Masson-Delmotte et al. (2021), Intergovernmental Panel on Climate Change (IPCC).

2
Guided by mounting scientific evidence, the United Nations Secretary of General announced that “Theera of global warming has ended. The era of global boiling has arrived.” (Guterres, July 2023).
3

I define a person as a dropout if he is not either employed (including self-employed), nor searching forjob (unemployed) and is not at school either.
4

From U.S. Bureau of Labor Statistics. See a nationwide trend in the U.S. at Figure 2.2.

5

A series of factors including technology (Autor, Levy and Murnane (2003); Acemoglu and Restrepo(2020)), free trade (Autor, Dorn and Hanson (2013)) and institutions (Autor and Duggan (2003)) have been
explored. See related literature section for greater detail.

2

regularly outdoors in the post war period. Intriguingly, over 95% of outdoor workers havebeen consistently occupied by males. In parallel, during 1970-2019, I compute that hot days
per year (daily temperature above 75F) experienced for an average U.S. resident increased
by 25.4 days. More exposure to hot days would physiologically augment labor costs ofmanual tasks while standing, walking and sweating, and thus, suppress labor supply foroutdoor jobs (discomfort effect)6 . Intriguingly, since the 1960s, residential air conditioners(Biddle (2008)) and TV sets became increasingly prevalent at home and relative cost ofworking outdoors vs. staying at home expanded. Simultaneously, hot days would hurt laborproductivity (Somanathan et al. (2021); Chen and Yang (2019)), and thus, shrink labordemand of outdoor jobs (productivity effect). Given fading incentives of working outdoors
via both forces, global warming would nudge workers to get out of the market, even if he is
not conscious of climate change. In the age of global boiling (Guterres, 2023), the reasoning
conjures up an old cautionary tale of frogs in the boiled water7 .
To assess the mechanism above, I build a panel of climate change across 722 U.S. commuting zones more than half a century (1960-2019). Containing a variety of climate zones,
the continental U.S. provides an ideal testing ground for gauging the climate impact. I construct a series of daily weathers (e.g., humidity; precipitation; snowfall) of commuting zones
from raw station records of nearly 15,000 U.S. weather stations from the National Climatic
Data Center (NCDC). To proxy daily mean temperature, nearly all the literature uses themean of daily maximum and minimum temperature. However, this is designed to significantly underrate day time temperature, a prime labor hours of outdoor workers. Combining
hourly temperature fluctuation computed from separate station records in the U.S. Climate
Normals, I find that median temperature during business hours (8 am - 6 pm) has been
substantially higher, especially in the summer, than the conventional all hour daily mean.
Combined with LFPR of prime-aged males computed from Census and American Com6

In the conventional labor supply model of consumption and leisure, utility from leisure is inverse-proxyof work costs.
7“If you throw a frog in a pot of boiling water, it will hop right out. But if you put that frog in a potof tepid water and slowly warm it, the frog doesn’t figure out what going on until it’s too late.” This talesuggests that people may react to acute shock, but fall into inaction under incremental change. (Old proverb,
in Meyer (2008))

3

munity Survey, this near-exogenous treatment permits a natural experiment, after controlling for extra climatological variables, demographics, health and wealth covariates, and labordemand-side variables in addition to commuting zone fixed effects. The rich regional and dynamic variation of analysis allows for inclusion of state-by-year fixed effects, capturing any
time-variant statewide institutions affecting both labor demand and supply (e.g., welfare
system; minimum wages; health care; unionization).
The baseline results suggest that 10 more hot days (temperature >75F) in decadal baselines shrinks prime-aged male LFPR by 0.2 percentage points. Cold days (temperature <40F) are comparably harmful, but with less precision. Interacting a start-of-period shareof outdoor jobs, I find that a channel of occupational climate exposure accounts for substantial portion of overall impacts. The response is sharper for younger males, and waslimited for prime-aged females, presumably because a very few (2-4%) working-age females
work outdoors. Aggregating impacts from change in both hot and cold days, I simulate that
decreasing cold days slightly upheld the participation before 2000. After 2000, by contrast,
climate change accounted for approximately 10-15% of prime-aged male LFPR drop. I alsofind that climate change significantly reduced only outdoor (not indoor) employment-topopulation ratio, and raised an unemployment rate8 . I interpret that less educated males
working outdoors have less comparative advantage in indoor occupations, which are supposed
to be more intensive in ICT or communication skills.
Theoretically, climate impact on wages in outdoor jobs is ambiguous. On one hand, as
the work cost increase, we should expect that survivors’ wages rose from shrinkage of labor
supply. On the other hand, diminishing labor productivity would suppress their wages fromcontraction of labor demand. Using a worker-level analysis, I find that hot days suppressed
wages keeping else equal (interpretably, as compensating wage differentials of working outdoors) after controlling for as many observables as possible. Classic labor market model tells
us that the negative wage shift implies a relative dominance of labor demand shift. This
indicates that global warming plays a comparable role as canonical labor demand shocks (in8My unemployment measure is a ratio of the unemployed to regional prime-age population instead of
conventional denominator of labor force participants.

4

cluding ICT shocks, automation, and import competition), ubiquitous in nearly all developed
economies.
At first glance, it is challenging to isolate labor supply responses, because rising discomfort with heat would automatically hurt labor productivity. To isolate the discomfort effect,
I run a pair of exercises. First, I test how regional prevalence of residential amenity (e.g.,
air conditioner) affected the climate impact on LFPR. An identification assumption is that
residential air conditioner increases relative cost of work (or leisure utility) without harming
labor productivity. Expectedly, I find that prevalence of residential air conditioner augments climate impacts on dropouts.9 Second, I directly test whether climate change eroded
preference for work, using a repeated cross-sections of World Value Survey (WVS). Pairing
interviewees’ locations10 and regional climate change, I find that exposure to more hot days
significantly hurt their work ethic, expressed in nearly all work-related questions. Though a
respondent’s detail occupation is unavailable, the effect shows up only for prime-aged males,
more sharply for the less-educated. Collectively, I conclude that the climate-dropout hypothesis operates both labor demand and supply sides. Under accelerating global warming, thepaper suggests that outdoor labor market has been and will further be a hotbed of dropouts
and rising socio-economic inequality.
Related Literature Traditional research on global warming is centered around climate
impact on agricultural productivity (See Dell, Jones and Olken (2014) for a survey). First, mystudy complements a burgeoning line of literature uncovering climate impact on productivity
in manufacturing plants. Somanathan et al. (2021) (in India) and Zhang et al. (2018),
Chen and Yang (2019) (in China), Cachon, Gallino and Olivares (2012) (in U.S. automobile
industry) showed higher temperature hurt labor productivity.11 Using an employer-side
survey, the papers documented declining labor productivity, partially by absenteeism, while
9

The finding resonates with Aguiar et al. (2021), assessing the role of video game technology to augment
leisure utility and depress labor supply of young males.
10Interview locations are available by longitudes and latitudes in the latest 2017 wave. See Section 3.4 forgreater detail.
11

In the field of engineering, laboratory studies show that temperature affects the productivity of office
work (Seppanen, Fisk and Lei (2006)) and academic performance of kids (Wargocki and Wyon (2007)).

5

my study uses population survey to explore individual labor supply. In contrast to theirfocus on manufacturing, where I find working indoors is predominant (albeit not necessarily
air-controlled), my paper features outdoor workers spread across sectors.
Second, my study also builds on a few recent papers on climate impact on labor demand adaptations on the employer side. Across U.S. counties, Ponticelli, Xu and Zeume(2023) showed temperature shocks increase energy costs and lower the productivity of small
manufacturing plants, and lead to higher production concentration in large plants. Using
establishment-level data, Acharya, Bhardwaj and Tomunen (2023) showed that firms operating in multiple counties cut employment in the warming county and increase it in unaffected
counties, whereas single-location firms just shrink. My paper shows adverse climate impacts
on regional employment rates and wages of outdoor workers, indicating a relative labordemand shift for outdoor workers.
Third, the paper gives a new climate perspective on the long-standing literature of declining males’ labor market attachment. Literature largely attributed the declining attachment
to shrinking labor demand for non-colleged workers (See Juhn (1992); Acemoglu (2002); Cardand DiNardo (2002)), later exemplified by skill-biased technical change of computerization(Autor, Levy and Murnane (2003)); skill-replacing technology of automation (Acemoglu andRestrepo (2020); Lerch (2020); Grigoli, Koczan and Topalova (2020))); free trade (Autor,
Dorn and Hanson (2013)) and offshoring (Harrison and McMillan (2011); Ebenstein et al.
(2014)).12 In addition to a series of labor demand shocks, the paper introduces climate
shocks across commuting zones——most likely a combination of regional labor demand andsupply shocks.
Fourth, this paper also explores labor supply mechanism with a similar spirit to Graff Zivin
and Neidell (2014).

Using time use diaries in American Time Use Survey, 2004-2006,

Graff Zivin and Neidell (2014) found that daily extreme weather shocks altered daily time
allocation, by shrinking labor hours or shifting outdoor leisure to indoor. To the best of myknowledge, this is the first paper to explore the effect of climate change on the conventional
12

In contrast, Parsons (1980) and Autor and Duggan (2003) showed expansion of Social Security disability
insurance benefits as a inhibitor for labor supply.

6

labor supply decisions.
The paper is organized as follows. Section 2 presents the data and proxies used for
empirical analysis. The estimation results are provided in Section 3. Section 4 assesses ofclimate impacts and investigates other mechanisms. Section 5 concludes.

2

Data

To empirically isolate the climate impact, I assembled a panel of climate exposure, labor
supply, and other labor market variables, together with other regional correlates during ahalf century during 1970-201913 . Along with Autor, Dorn and Hanson (2013), Autor andDorn (2013) and Acemoglu and Restrepo (2020), I use a commuting zone (or CZ, Tolbert andSizer (1996)).14 Given cross-county commuting, commuting zones most likely contain both
workplaces and commuting routes of each worker, serving as a unit of regional labor market.

2.1

Climate change

The weather station data are drawn from Global Historical Climatology Network Daily(GHCN-Daily) from the National Climatic Data Center (NCDC) of the National Oceanicand Atmospheric Administration (NOAA). GHCN-Daily is an integrated database of daily
climate summaries from land surface stations and contains the most complete collection of US
daily climate summaries available under universal quality assurance checks. I use weather
variables of the daily d’s maximum and minimum temperature Tdmax , Tdmin , precipitation,
snowfall and dew points. Following the literature, I construct a daily temperature Td as aweighted average of these two s.t. Td = ωTdmax + (1 − ω)Tdmin where ω ∈ (0, 1). Nearly all
the literature uses ω = 0.5, however, taking a simple mean significantly underrates day time
temperature.
13

Outcome years include 1980, 1990, 2000, 2010, 2019, excluding 2020 as the onset of pandemic. Start-ofperiod controls for each outcome period is 1970, 1980, 1990, 2000, 2010, respectively.
14

On the operational front, commuting zones are a combination of multiple counties and a finest geography
units to consistently measure LFPR since 1980 from the public data. Only employment-to-population ratios
are available for counties by BLS.

7

To see this, I compute a weekly CZ-specific ωweek,month,l to match the median temperature during the typical business hours (8am-6pm), employing within-day hourly temperaturefluctuation from Climate Normals dataset (from National Centers for Environmental Information).15 A seasonal distribution of ωweek,month,l is found to be substantial: a median ω is
0.8 in the summer vs. 0.68 in the winter. (Figure 1; left) Driven by a strong seasonalityof within-day temperature cycle, temperature in the labor market are supposedly higher
than the conventional daily mean with ω = 0.5, substantially underrating the temperature
exposed to outdoor workers during daytime. (Figure 1; right). The paper is the first to
construct a measure of climate change exposed to workers across regional labor markets.

Figure 1: Distribution of daily temperature weight across seasons (left) and annual distribution of temperature by weight choice (right)
Note: (left) A unit of observation is ωl,t , computed in weekly average among four weeks within each month,
constructed from station records by Climate Normals, 1980-2010. Each year is split to four seasons: Summer;
June, July and August. Fall; September, October and November. Winter; December, January and February.
Spring; March, April and May. (right) A unit of observation is a daily temperature averaged during 20112019, allocated to each bin. ωl,t = 0.5 is an arithmetic mean of max and min temperature of Global Historical
Climatology Network Daily, while weekly ωl,t is set to fit median temperature during 8am-6pm using Climate
Normals. (See text for more details.)

To construct CZ-level climatological variables, I follow an inverse-distance weighted
15
See Appendix for details. Instead of targeting Tdmedian , using Tdmean does not significantly change theestimates. In the robustness check section, I shall test the robustness of the choice of ω.

8method to aggregate each station-level data record (e.g., Barreca et al. (2016) and manyothers) Limiting to weather stations with complete records in any given year, records fromclosest 10 stations from each CZ population centroid16 is aggregated weighted with an inverseof squared power of distance from the centroid. Climate is characterized by a distribution ofrealized daily weathers. I proxy climate change as decadal prior average of a number of hot
and cold days, with mean daily business hour temperature cutoffs 75F and 40F, respectively.
I obtain a substantial geographic variation of temperature dynamics between and withinclimate regions and states, especially in large states (e.g., Texas and California). Proxying
climate change as a decadal trend of hot days (mean temperature >75F) and cold days (<40F), I document a dramatically rich between- and within-state variation of climate change,
where some regions experienced even cooling17 .
The initially hot regions (especially Southeast, South and Southwest) experienced theseverest warming and cold regions (Northeast, Northwest) experienced the mildest warming, or even, cooling with decreased number of hot days.18 Notably, regional climate shocks
are both conceptually and geographically distinct from conventional labor demand shocks,
including ICT shocks (indoor occupations with routine analytical tasks across sectors), globalization (import competition with Chinese products) and automation (industrial robots inprimarily automobiles and electronics, by Acemoglu and Restrepo (2020)).

2.2

Labor supply and market outcomes

Figure 2.2 summarize the long-run nationwide trend of climate change and LFPR of primeaged males. I compute that climate change loomed up in the late 1960s in the U.S.. The
warming trend runs in parallel to their declining LFPR, taken from the Bureau of Labor
Statistics (BLS). ()16

Population centroids at CZ-level are constructed as population-weighted averages of county-level population centroid longitudes and latitudes available from the Census Bureau. (See Appendix for details)
17

Figure in Appendix shows change of hot days from 1961-1970 to 2011-2019.

18

The distribution of days across daily mean temperature bins is single-peaked in nearly all areas. Supposethat climate change is a uniformly upward shift of temperature distribution. Then, climate change increases
the number of hot days more quickly in initially hotter area.

9

Figure 2: Nationwide trend of annual hot days (left axis; 10 year moving average) and labor
force participation rate of prime-aged males (right axis; 1960-2019, U.S.)
Note: Weather station records from National Oceanic and Atmospheric Administration (NOAA) are aggregated to county-level temperature, weighted by county-level population. Nationwide hot days (red line) is a
10 year moving average of county-level exposure to hot days, where hot days have median temperature of 75F(23.9 C) and above during 8am-6pm (See construction of temperature data for the main text.) Nationwide
participation rate and employment rate of prime-aged ( 25-54 years) males are taken from U.S. Bureau ofLabor Statistics.

In light of regional analysis, I construct commuting zone level LFPR and wages and otherregional covariates for prime-aged (age 25-54) males. The age scope largely exclude theconcern of adjustment margins by education and retirement. I use repeated cross-sectional
surveys of IPUMS of Census (1950-2000, by decade) and American Community Survey (2010
and 2019)19 . These datasets include between 1 and 5 percent of the U.S. population andprovide a comprehensive set of information at the individual level with socio-demographics
characteristics and labor market attachment. A data offers the place of residence at thehousehold level, permitting a linkage to climate variables20 . All the analysis is limited to
non-institutional samples in the U.S. mainland.
19

To secure the sample size, I followed the literature to stack each ACS samples with 2-year sample of2009-2010 and 2018-2019, respectively.
20

The tiniest geographic units from the IPUMS are county groups or Public Use Microdata Areas. Using
Dorn (2009)’s crosswalk of these areas and CZs with a probabilistic weight, I translated these areas to CZs,

10

In 1970, most of commuting zones had a high LFPR above 90%. In 2019, however, theU.S. underwent a significant LFPR drop, with great regional divergence: initially hot andwarming areas (Southeast and South) suffered from significant LFPR drops, while initiallycold with mild warming areas (Northwest, Northeast) kept relatively higher LFPR, offering
an visual impression of a climate-induced dropout hypothesis.

2.3

Outdoor jobs

To directly proxy a person working outdoors regularly, I probe occupational requirements ofwork environments, following task-based approach (e.g., Autor, Levy and Murnane (2003);
Dingel and Neiman (2020)). I use Work Context survey of O*NET (Occupational Information Network by the US Department of Labor), containing hundreds of standardized andoccupation-specific descriptors on nearly 1,000 occupations, collected from median 26 respondents per occupation. In “physical and social factors that influence the nature of work”,
I use a question of How often does this job require working outdoors, exposed to all weather
conditions? The answer is from 5 choices: 5. Every day; 4. Once a week or more but not every day; 3. Once a month or more but not every week; 2. Once a year or more but not every
month; 1. Never. Combining answers 4 and 5, I compute an occupation-level likelihood that
where a person regularly works outdoor at least weekly across 873 ONET-SOC occupations.21
Moreover, analogous questions on indoor environments were asked separately whether each
workplace temperature is non-controlled or controlled. I connect an ONET-SOC identifier
to occupation code in the Census and American Community Survey 22 .
Table 1 summarizes the ranking of major occupations (over 1 million employment in2019) with a highest and lowest likelihood of working outdoors. One can see that all top 10
occupations are predominantly served by males relative to least outdoor occupations.
Figure 3 (left) shows propensity to work outdoors within sectors and sectoral composition
21
Using Work Context Survey from the O*NET, Dingel and Neiman (2020) defined a job which can bedone at home. Conceptually, jobs which can be done at home and outdoor jobs are mutually exclusive, but
not exhaustive. Jobs at indoor facilities (e.g., restaurant waiters; janitors; laboratory scientists) away fromhome do not belong to either categories.
22

See Appendix for details.

11

Table 1: Occupation rankings of outdoor workers (2019)

Note: Rankings are made by a ratio of employees working outdoors everyday from Work Context Survey,
within occupations with 0.5 million annual employment, computed from IPUMS of ACS, stacked in 20182019 and population-weight adjusted in 2019.

Figure 3: Within-sector share and sectoral composition of outdoor workers

Note: Computed from IPUMS of Census 1970, 1980, 1990, 2000 and ACS 2008-2010 (for 2010) and 20172019 (for 2019). The outdoor worker regularly operates outdoors at least once a week identified by a Work
Context Survey from the O*NET (See the main text).

of outdoor workers. Over 50% of agriculture and one-third of construction and transportation sectors, a fifth of Mining/Utility and transportation workers, and less than 10% of
manufacturing and service sector employees work outdoors. Given that heat-sensitive sectors include a substantial portion of non-outdoor workers, an industry is a crude taxonomy
12to characterize outdoor employment. By contrast, Figure 3 (right) shows the sectoral composition of outdoor workers. Although agriculture and construction consistently accounted
for a sizable 40-50%, one can see that outdoor jobs widely spread across primary, secondary
and tertiary sectors, indicating that conceptually distinct category from conventionally-usedindustries.
I then proceed to document who works outdoor. Figure 4 illustrates a share and composition of outdoor workers by sex and education attainments. Figure 4 (top; left) shows that
20% of males vs. 2% of females (aged 16 and above) work outdoors. As 90-95% are served
by males, it is safe to presume that outdoor jobs are pre-dominantly “male occupations”.
(top; right)
I compute that prime-aged (25-54) workers, a primary scope of this study, have consistently accounted for 70-80% of outdoor workers. Limiting to prime-aged males (bottom),I analogously document the share of (left) and composition by (right) workers’ education
attainment. Intriguingly, non-college workers increasingly work outdoors. This is consistent
with labor market polarization (Autor and Dorn (2013))23 that middle-wage employment
with routine tasks, most of them working indoors, were absorbed in low-wage non-routine
manual occupations, exemplified by outdoor jobs. Collectively in Figure 4, the outdoor workers are predominantly prime-aged less educated males. High-school graduates and dropouts,
who consists of the majority of labor market dropouts, kept accounting for 70% of outdoor
workers for 3 decades since 1990.

3

Analysis

3.1

Empirical Model

Armed with rich regional of climate change, I identity the impacts of global warming on
regional labor supply. An identification assumption is that a daily weather is randomly
drawn from global meteorological data generating process under geographical primitives
23

Intriguingly, the total share of outdoor workers is fairly stable despite the rising share of working outdoors
for less educated males. This is reconciled by a decline of high-school dropouts and rise of college enrollments.

13

Figure 4: The demographics of outdoor workers (proportion (left) and composition (right)of outdoor workers by sex and education groups)
Note: Computed from IPUMS of Census 1970, 1980, 1990, 2000 and ACS 2008-2010 (for 2010) and 2017-2019 (for 2019).

Employment of outdoor worker is computed by a Census/ACS sample weight multiplied with a likelihoodof regularly working outdoors at least once a week recorded in Work Context Survey from the O*NET (Seethe main text).

14

(e.g., elevation and distance to coasts).24
To isolate the effect of climate change from other correlates, I build a following binned
specification for a demographic group g (a baseline sample g is prime-aged (25-54) males)
across CZ l and periods h = (h, h] ∈ {(1960, 1970], (1970, 1980], (1980, 1990], (1990, 2000], (2000, 2010], (2010, 2019] :
g
yl,h
=

16
X

β b daysbh + Cl,h + Xgl,h + Bl,h + δl + δs,h + l,h

(1)

b=1
g
is a l’s outcome (e.g., LFPR or wages) in group g at the end of period and daysbh
where yl,his number of days with mean business hour temperature averaged during the period, falling
into 16 bins b ∈ {(0, 20), [20, 25), · · · , [65 − 70), [75 − 80), [80 − 85), [85 − 90), [90, 100)}.25
As an annual sum of bins is constant, I take [70,75) Fahrenheit (21.1-23.9 Celsius) 26 asa baseline bin and omitted from the model. Cl,h includes other climatological variables
except temperature: precipitation27 , snowfalls, and wind. Xgl,h contains a rich vector ofstart-of-period demographic composition of a group g (gender, race and ethnicity, age) andwealth factors (e.g., private non-labor income). As warming areas might have simultaneously
received adverse labor demand shocks (e.g., technology or free trade shocks), I explicitly
control labor demand shifter stemming from nationwide sectoral growth by a shift-share
Bartik shock Bl,h . Specifically, following Goldsmith-Pinkham, Sorkin and Swift (2020),
Bartik shocks for a proxy z (employments or wages) during a period h are constructedPzz
by Bl,h= k sl,k,h0 ηk,h, where sl,k,h0 is a start-of-period industry k-location l employment
24

Temperature records in weather stations might be influenced by buildings in urban cities (c.f., heatisland phenomenon). No evidence was found that either hot or cold days are affected by regional population
density. Results are available on requests.
25

For leap years, the number of days in each bin is adjusted by multiplying 365/366

26

The choice of baseline bin to neighboring bins of 65-70F or 60-65F does not significantly change theshape of non-linearity. Graff Zivin and Neidell (2014) specifies the breakpoint of hot day as similar to mine,
25C. Chen and Yang (2019) uses 21-24C as baseline bin in China, very close to mine.
27

Alternative formulation of days with low, medium and heavy precipitation does not significantly change
the estimates.

15

zshare28 and ηk,his a national industry growth rates of z in the U.S. mainland.29 In addition

to CZ fixed effects δl , CZ-level dynamic variation allows for inclusion of state-year fixed
effects δs,h , containing any statewide institutions (e.g., minimum wage; welfare system),
independently shaping individual labor supply. Therefore, all the estimates of the study
generates from within-state cross-CZ variation of typically 14-15 CZs within each state. l,his a normally distributed error term. Because weather variables are spatially correlated,
standard errors are clustered at CZ levels. The model is weighted by start-of-period CZ
share of national population. β b is an estimand of interest, interpreted as replacement of 10
hot and cold days with the pre-set baseline temperature of bin [70-75) Fahrenheit.

3.2

Resultsgg
Semi-parametric bin estimates Setting LFPR as an outcome in (1) (s.t. yl,h
= LF P Rl,h)in the end year h of period h in response to climate change, Figure 5 illustrates estimatesof the semi-parametric bin model (1). Relative to an omitted bin 70-75F, I find clear nonlinearity of climate effects. 10 day (or business days in two weeks for full-time workers)
replacement of baseline bin to hot days above 75F significantly drops LFPR of prime-aged
males by 1.0 p.p.. Likewise 10 day replacement to cold days below 40F yields less significant,but negative impacts. Replacement to “normal days” with moderate temperature (40-75F)
does not significantly affect LFPR. Overall, I found sharper non-linearity for hot days.
28

I test a sensitivity of formulation of Bartik shocks within a variety of samples (less-educated (only highschool graduates and dropouts), males, less-educated males, prime-aged males, prime-aged less educatedmales, including self employers) The results, however, are largely unchanged.
29

Admittedly, Bartik shock formulation rules out a general equilibrium effect through nationwide climateinduced technical change from aggregation of regional climate exposure (e.g., California’s climate change
might affect the entire agricultural sector’s technology. (Moscona and Sastry (2022))). An identification
assumption is that each region is infinitesimal enough to affect the nationwide sectoral labor demand.

16

Figure 5: The impact of climate change on labor force participation rate (prime-aged males)
Note: (Top) Estimates of β b in equation (1) are depicted with 95% confidence intervals (red dashed lines).
Baseline bin is a 70-75F. (bottom) Days during 1971-1980 and 2011-2019 are allocated over 16 bins with
5F interval along a mean work hour temperature for a median U.S. resident. (Bottom) The national level
exposure in each bin is a regional average weighted with CZ-level population during periods. Dashed lines
are thresholds of hot (> 75F) and cold days (< 40F) in my main specification.

The non-linearity is perfectly aligned with prior works documenting the link between
temperature and agricultural productivity (Schlenker and Roberts (2009)), labor productivity (Somanathan et al. (2021)) and outputs (Burke, Hsiang and Miguel (2015)) and thelaboratory study (Seppanen, Fisk and Faulkner (2003)). The results are, however, in notable
contrast to climate-mortality literature reporting the intensive harm of the cold, especiallyon the elderly.30 One interpretation is that as outdoor workers, typically engaged in agriculture, construction, transportation and manufacturing sectors are often obliged to wear
PPEs (personal protective equipments, e.g., thick wears; helmets; hats; face masks; gloves)
as shields from occupation injuries, but making them vulnerable to heat stress.
To increase precisions of the estimates, I use a more parsimonious model featuring
30

Deschenes and Moretti (2009) found that cold days are responsible for mortality, relative to hot days.

17Pwith upper and lower ends of weather distribution, replacing the terms b=1 β b daysbh byPzz∈{hd,cd} β zl,h in (1), where hdl,h , cdl,h are number of hot and cold days at region l and period h, respectively:31 Guided by the 5F bin estimation, “hot days” with mean business hourtemperature above 75F and “cold days” with the one below 40F and days with 40-75F are
set as baseline “normal days”.32 β hd , β cd captures the climate effect of interest from warming,
interpreted as an impact of replacing 10 normal days by 10 hot or cold days, respectively.
Baseline estimates Table 2 summarizes estimates of a parsimonious model, separately
for prime-aged males vs. females.
Table 2: Climate change and labor force participation rates across commuting zones (primeaged population; 1980-2019)

Note: N = 3, 610 (5 time periods × 722 Commuting Zones). Limited to prime-aged (age 25-54) in the U.S.
mainland. Hot days have mean temperature of business hours (8am-6pm) over 75F, and cold days havethat of less than 40F, averaged during each period. The outcome years are 2019, 2010, 2000, 1990, 1980.
Labor force participation rate is computed from IPUMS of Population Census and American Community
Survey. All models include other climate variables (humidity, precipitation, snowfall), demographic variables(5 year age bins; a ratio of racial groups and immigrants; a ratio of veterans), and commuting zone fixed
effects. Robust standard errors are clustered by commuting zones. All models are weighted by start of period
commuting zone share of national population. *** p < 1%; ** p < 5%; * p < 10%.

31
The parsimonious model featuring both ends of extreme temperatures is widely adopted in the literature(e.g., Barreca et al. (2016); Somanathan et al. (2021))
32

See a robustness check for alternative thresholds of hot and cold days.

18

For prime-aged male samples, (1)-(6) models differ in the inclusion of controls. (1) includes only other climate variables (humidity, precipitation, snowfall), and demographic
variables (ratio of age bins, racial groups, immigrants, veterans). Based on (1), (2)-(6) adds
labor demand shocks (Bartik shocks), market structure (ratio of agriculture and manufacturing employment, unemployment rate), state-year fixed effects, disability ratio and non-labor
income (including family income and social security benefits), respectively. A preferred specification (6) with a full battery of controls indicates that decadal baseline shift of 10 more hotdays decreases the LFPR by 0.272 p.p. (p = 1.2%). To incorporate the effect of humidity to
hot days, I build a discomfort index (DI) based on the standard meteorological formula: if a
discomfort index exceeds 75, more than half of people supposedly feel discomfort. Replacing
hot days by discomfortable days (DI > 75) yields larger and more robust estimates.33
To contrast the results with females, (7) replicates the same full model (6) to prime-aged
female samples. The point estimate is negatively smaller, but not statistically significant.
(p = 13.6%). This is consistent with key presumption of climate effect manifest via outdoor
jobs, 2-4% of whom are females.34 Reassuringly, this sharp contrast is at odds with otherconfounding factors (automation, import competition and welfare-system) which operates ina more gender-neutral manner.35 I test the sensitivity of estimates with treatment window(5 year or 15 year moving average) and temperature cutoffs (combination of cutoffs 65F,
70F, 75F (hot days) paired with 30F, 35F, 40F (cold days). However, the effect of hot days
is similar., but the results are almost stable.
Employment, non-employment and unemployment To investigate the mode of market attachment, I further decompose LFPR to employment rate and unemployment rate36
in Table 3. (1) corresponds with the main specification (6) in Table 2.
33

See construction of DI in Appendix. Alternatively, Somanathan et al. (2021) construct WBT (wet bulb
temperature), combining temperature and humidity, and obtained similar larger results.
34

In contrast, this suggests the possibility of climate exposure under commuting time for all commuters.
I shall discuss the role of commuting time below.
35

ADH and AR report negative impacts on females as well.

36

My unemployment measure is a ratio of unemployed to regional prime-age population instead of labor
force participants.

19

Table 3: Climate change and labor market attachment across commuting zones (prime-aged
males; 1980-2019)

Note: N = 3, 610 (5 time periods × 722 Commuting Zones). Limited to prime-aged (age 25-54) in the U.S.
mainland. Hot days have mean temperature of business hours (8am-6pm) over 75F, and cold days havethat of less than 40F, averaged during each period. The outcome years are 2019, 2010, 2000, 1990, 1980.
Labor force participation rate is computed from IPUMS of Population Census and American Community
Survey. All models include a full battery of controls in Table 2 (6) and commuting zone and state × year
fixed effects. Robust standard errors are clustered by commuting zones. All models are weighted by start ofperiod commuting zone share of national population. *** p < 1%; ** p < 5%; * p < 10%.

Employment rate shows even stronger estimates -0.393 (p < 0.1%) in (2) than LFPR
in (1). Within employment rates, salaried employment received severest results both ineconomic and statistical significance -0.570 (p < 0.1%) in (3), while the effect on selfemployment, including self-employees working at home37 was positive 0.177 in (4) and
0.072 in (5), respectively (p < 5%). Interpretably, flexibility of work arrangement in selfemployment, especially at home, permits adaptation for climate shocks.
Theoretically, climate’s effect on an unemployment rate is ambiguous. On one hand,outdoor workers become unemployed for search for a previously second-best alternative job.
On the other hand, unemployed workers might drop out by quitting job search from poor
option value of outdoor jobs and increased search costs.38 The estimates in (6) shows positive
estimates for an unemployment rate, indicating that in net, employments are pushed out tothe unemployment pool.
Within non-employment, I specify a dropout rate, defined as a number of dropouts (not37

I use an answer of “working at home” in a question on commuting mode.

38
As the climate gets warmer, unemployment cost would increase because job search requires a series ofoutdoor activities (e.g., going to job agency; interviews), especially before the Internet era.

20

in employment, unemployment nor education) per population sample of study. Climatechange increased a dropout rate with estimates 0.221 (p < 5%) in (8). However, the effect ispartially offset by increase of full time student ratio. Given that the analysis only considers
males over 24 years old, this might be somewhat surprising, but is consistent with my theory
if school buildings (possibly at college campus) offers shelters from climate change.
Channel via outdoor tasks To isolate the effects via outdoor labor market, I explore
whether climate impact reacts with regional exposure to outdoors. Operationally, I interact
temperature bin variables β z zl,h with an initial dependency on outdoor jobs as β z zl,h ×rhout .
Limiting to the key driver of employment rate, Table 4 summarizes the result.
Table 4: Climate change and labor market attachment via outdoor exposure and prevalenceof residential air conditioners across commuting zones (prime-aged males; 1980-2019)

Note: N = 3, 610 (5 time periods × 722 Commuting Zones). Limited to prime-aged (age 25-54) in the U.S.

mainland. Hot days have mean temperature of business hours (8am-6pm) over 75F, and cold days havethat of less than 40F, averaged during each period. The outcome years are 2019, 2010, 2000, 1990, 1980.
Labor force participation rate is computed from IPUMS of Population Census and American Community
Survey. All models include a full battery of controls in Table 2 (6) and commuting zone and state × year
fixed effects. Robust standard errors are clustered by commuting zones. All models are weighted by start ofperiod commuting zone share of national population. *** p < 1%; ** p < 5%; * p < 10%.

In (1)-(3), each interaction term show theoretically-predicted estimates for LFPR, employment rate and dropout rate, respectively. Reassuringly to the story, this indicates regions initially dependent on outdoor jobs saw larger subsequent impacts from climate change.
Outdoor workplace significantly shifted up the response. Interacting start-of-period outdoor
21

exposure with estimates, a simulation shows that the channel via outdoor market accounts
for roughly 60-70% of the overall climate impact on LFPR.
Since the late 1960s, residential air conditioners expanded the relative cost of working
outdoors vs. staying at home presumably spiked in the same period. Theoretically, airconditioners would increase opportunity cost of labor by upgrading the value of leisure athome, without harming daily labor productivity of outdoors39 . Following the extrapolation
strategy of Barreca et al. (2016), I impute a CZ-level adoption rate of the list of household
appliances computed from Census of Households in 1960, 1970, 1980.40 To test the roleof residential air conditioners for climate’s impact, in (4)-(6), I interact hot and cold days
with an adoption rate of residential air conditioner. All show significantly complementary
estimates only with hot days, suggesting that improved indoor environment served as an inhibitor of labor supply.41 Given that air conditioners augment relative discomfort at working
outdoors especially in summers, this is one evidence that discomfort effect is at work.
Heterogeneous analysis by demographic sub-samples The model predicts that climate effect is exclusive for less-educated workers. To see the heterogeneity of impacts, I reestimate the model, splitting to subsamples of education attainment group g ∈ {HS dropouts,
HS graduates, some colleges, college graduates}with group-specific controls Xgl,h , in Table
5.
Aligned with the theory of outdoor labor mechanism, the effect is more salient to lesseducated males, especially high-school dropouts (-0.684 (p < 1%)). Coefficients for workers
with above high-school degrees (3)-(5) are not statistically significant, consistent with their
few presence of outdoor workers and richer outside options in indoor jobs. The heterogeneous
impacts indicate that the impact of near-random climate shocks is far from random, butsystematically regressive within nations.
Analogously, I ran the same model by each age group sub-samples g ∈ {below25, 25 −39

Employing monthly mortality across states during the twentieth century, Barreca et al. (2016) show that
extreme hot days have been responsible for the mortality, but the relationship is mitigated by air conditioner.
40

After 1980, extrapolated values are bounded by 100%.

41
Admittedly, the analysis might be hampered by endogenous adoption of air conditioner (Biddle (2008)),
depending on regional weathers or labor supply.

22

Table 5: Estimates by education attainments and age groups (males; 1980-2019)

Note: N = 3, 610 (5 time periods × 722 Commuting Zones). Limited to prime-aged (age 25-54) in the U.S.

mainland. Hot days have mean temperature of business hours (8am-6pm) over 75F, and cold days havethat of less than 40F, averaged during each period. The outcome years are 2019, 2010, 2000, 1990, 1980.
Labor force participation rate is computed from IPUMS of Population Census and American Community
Survey. All models include a full battery of controls in Table 2 (6) for each sub-samples and commuting zone
and state × year fixed effects. Robust standard errors are clustered by commuting zones. All models areweighted by start of period commuting zone share of national population of each sub-sample. *** p < 1%;** p < 5%; * p < 10%.

34, 35 − 44, 45 − 54, 55 − 64}. Intriguingly, the effect is largest for middle-aged workers (aged35-44) in the prime-aged males, even after controlling for education. This is alarming fortheir running a career and providing a sufficient environment for children, if any. The effectis insignificant for outside prime aged males (aged 18-24 young males and aged 55-64 seniormales). This is also consistent with the declining share of outdoor occupations for younger
workers during 2000-2019This could indicate the aging of outdoor workers. The cold days
are more harmful for non-participation (or early retirement) of older workers (above 65),
consistent with the literature to connect extreme cold days and mortality for the elderly.42
42

Deschenes and Moretti (2009) document that extreme cold, not extreme hot days are responsible formortality.

23

3.3

Outdoor vs. indoor labor market

The key prediction of the theory in response to climate change is that outdoor workers would
drop out of the market, or potentially, transfer to indoor jobs. This section directly tests
this prediction.
Employments and wages in outdoor vs. indoor occupations I test weather climate change affected employment-to-population ratios of outdoor and indoor jobs. Using
Work Context Survey questions, indoor jobs are split into controlled vs. non-controlledenvironments. Setting the employment rate of each job in the main specification, Table 6
summarizes the results.
Consistent with the theory, I find that an outdoor employment rate significantly shrunk.
Intriguingly, indoor uncontrolled workplaces show modestly negative estimates in (4), plausibly because heat and humidity are easier to be retained indoors, albeit sheltered fromrainfall and wind.43 By contrast, an employment rate with indoor controlled workplaces
show a statistically insignificant, but negative point estimate. Three takeaways are worth
noting. First, transfers from outdoor to indoor workplaces appears to be limited plausibly
because skills in outdoor cannot be transferrable to indoor occupations. A part of displaced
workers shifted to low-skilled indoor service jobs (e.g., health care workers; waiters) withmuch higher ratio of female workers, relative to outdoor jobs.
Second, a sharp contrast between (2) vs.

(3) corroborates the role of temperatureand humidity for workplaces for labor cost or productivity. This is also consistent withestablishment-level study of Somanathan et al. (2021), showing larger drop in productivity
in plants without air conditioners compared to controlled plants. Third, a negative point
estimate for indoor workers is suggestive that increased hot days might raise the commuting
cost. Intriguingly, cold days show significantly negative estimates, consistent with Cook andHeyes (2020) reporting cognitive performance in cold days in Canada. Using Census and
ACS, I compute that an average commuters spend 45 min. per day, consisting for 12% of
43This also indicates that air pollution is less likely to be a confounder, as indoor environments are shieldedfrom pollution generators. Aside from temperature, Graff Zivin and Neidell (2012) showed the air pollution’s
impact on labor productivity of agriculture workers.

24

Table 6: Climate change and employment rates across climate exposure (prime-aged males;
1980-2019)

Note: N = 3, 610 (5 time periods × 722 Commuting Zones). Limited to prime-aged (age 25-54) in the U.S.

mainland. Categories of occupations are based on O*NET Work Context Survey. See text for definitions.
Hot days have mean temperature of business hours (8am-6pm) over 75F, and cold days have that of lessthan 40F, averaged during each period. The outcome years are 2019, 2010, 2000, 1990, 1980. Labor force
participation rate is computed from IPUMS of Population Census and American Community Survey. All
models include a full battery of controls in Table 2 (6) and commuting zone and state × year fixed effects.
Robust standard errors are clustered by commuting zones. All models are weighted by start of period
commuting zone share of national population of each sub-sample. *** p < 1%; ** p < 5%; * p < 10%.

their weekly labor hours.
To test the effect of weathers on outdoor occupations, Work Context Survey permits
further detailed illustration of outdoor and indoor workplaces.44 Using the proxy of Outdoors, Under Cover (e.g., gas station; hot dog stand), I separately consider outdoor workers
into outdoor workplaces with and without roofs ((4) and (5)), which are supposedly block44

The original survey questions at Work Context Survey are the followings: How often does this job require
working outdoors, under cover (e.g., structure with roof but no walls)? How often does this job require working
in an open vehicle or equipment (e.g., tractor)? How often does this job require working in a closed vehicle
or equipment (e.g., car)?

25

sunshines, precipitations and snowfalls. The estimates of outdoor workers with roofs in (4)are slightly negatively smaller in magnitude than in (5), but largely comparable. The results
indicate that sunlight (containing ultraviolet lights) might be a partial confounder, but does
not play a central role.
Some outdoor workers use enclosed vehicles (e.g., truck or taxi drivers; mail carriers),
potentially with an air conditioner. To isolate the workers working at vehicles, I use aproxy of In an Enclosed Vehicle or Equipment as well as In an Open Vehicle or Equipment(e.g. tractors; motorbikes; bicycles) without air conditioner. Perhaps surprisingly, both
employment rate of both closed and open vehicle jobs decrease with climate change with acomparably negative impact. This suggests that in enclosed vehicles, air conditioners areeither not installed or its efficacy is limited (i.e., frequent door openings). Moreover, giventhat the majority of commuters use enclosed vehicles (cars; trains; buses), the result also
signals that climate change would increase commuting costs, putting downward pressure tofemale LFPR ((7) in Table 2).
In parallel to climate-induced shifts in employment rates across occupations, I examine
how wages in outdoor vs. indoor occupations responded with climate change by a worker
level analysis. Limiting to full-time full-year workers and excluding self-employees, I compute weekly wages, constructed by annual labor income divided by weeks worked during
a year. I control for individual demography and education, and three-fold fixed effects in
CZ-occupation group, state-year and occupation group-year levels. Table 7 shows a significantly negative impact on wages only for outdoor workers. A standard labor market model
suggests that the negative effect on wage indicates the relative dominance of labor demand
shift compared with labor supply shift.45 Climate change should be comparable to otherconventional labor demand shocks, such as technological revolution and free trade.
45

Deryugina and Hsiang (2014) reported the negative impact of temperature on mean annual income across
U.S. counties. By contrast, my analysis only found an adverse impact on weekly wages and annual incomeof outdoor workers.

26

Table 7: Climate change and weekly wages across climate exposure (prime-aged males; 19802019)

Note: N =X. Limited to prime-aged (age 25-54) salaried full-time full-year employees in the U.S. mainland.

Categories of occupations are based on O*NET Work Context Survey. See text for definitions. Hot days
have mean temperature of business hours (8am-6pm) over 75F, and cold days have that of less than 40F,
averaged during each period. The outcome years are 2019, 2010, 2000, 1990, 1980. Weekly wage is computedfrom IPUMS of Population Census and American Community Survey. All models include an experience,experience squared, a dummy of races and immigrants, veteran, education attainments, interacted with ayear dummy. All models also include commuting zone, state × year, occupation group × year fixed effects.
Robust standard errors are clustered by commuting zones. All models are weighted by sampling weight shareof each subsample employment. *** p < 1%; ** p < 5%; * p < 10%.

3.4

Labor supply mechanism

The response of wage is informative to relative dominance of labor demand side. To elicit
discomfort effect from labor supply side, I directly test whether climate change hurt preference for work measured by a series of questions at World Values Survey (WVS). WVS
2017 wave started to record a longitude-latitude of each respondent, allowing for connection
with regional temperature and their affiliation of commuting zones. I collect 5 questions
regarding work ethics and questions on Happiness and Wellness module. As each question
has different number of choices, I normalize the answers to 0 (most negative response) to
1 (most positive response). (For raw description of survey questions, see Appendix.) I test
how an experienced trend of hot and cold days affected their work ethics, controlling for27their demography, education and state fixed effects. The estimates comes from within-statevariation.
Table 8: Climate impact on work values, subjective well-being (prime-aged males; 2017)

Note: Prime-aged (25-54) males in World Value Survey, 2017. Main climate variables are 10 year average of

hot days and cold days in the year before (2007-2016). See text for interpretation of each outcomes. Other
individuals controls include age, age squared, and dummies of race, immigrant and education attainments.
Start of period commuting zone level controls include commuting zone population density, share of agriculture
employment, a ratio of above 65 population, a ratio of people born in the same state, and a ratio of renting
a house. All the models include state fixed effects and weighted by a sampling weight of WVS. Standard
errors are clustered by states. *** p < 1%; ** p < 5%; * p < 10%.

Table 8 summarizes the results. In Panel A, I find that experienced hot days significantly
hurt motivation and work ethics of prime-aged male sample, despite its relatively small
sample size. A significantly negative responses was found in (1), (2), (3), (5) with an weaker
exception is (4). In Panel A, each question has different format of multiple choice. The
response was not observed for other samples of prime-aged females, older (above 55) males
and younger (under 25) males. Though WVS does not identify outdoor workers, the effect isstronger for less-educated and younger workers, consistently with my main regional analysis.
28

This is consistent that response comes from occupational exposure to climate change. PanelB also showed adverse outcomes on subjective well-beings (with a life satisfaction gives aweak estimate). Especially, financial satisfaction (9) and its comparison to parents in thesame age (10) show significantly large negative estimates, consistent with the analysis ofclimate shocks above.
The data is cross-sectional, and thus, I cannot entirely rule out the possibility that lowmorale adult males systematically self-select warming locations within states. However, I
view selection bias is limited given that WVS near-randomly select respondents along thenational demographic composition. Collectively, this auxiliary analysis suggests that climatechange undermined the social norm for labor force attachment.

4

Assessment of impacts

4.1

Climate impacts

Armed with the empirical model and estimates in Section 3, this section assesses the contribution of climate change for drop in LFPR. To see whether the climate impact changes overtime and adaptation for extreme weathers operated, I interact a period dummy (1980,1990,· · · , 2010, 2019) with hot and cold days in the main specification. The impact of hot days
slightly improved as -0.287 (1980s), -0.261 (1990s) -0.255 (2000s), however, the latest period
experience the severest harm, -0.354 (2010s). By contrast, the impact of cold days shrinks
overtime. Consistent with Deryugina and Hsiang (2014), any modes of adaptation (i.e., migration, reallocation, business exits, innovation) is limited. This might also signal the lackof heat regulation in contrast to extreme cold in the workplace.46
Given dynamically changing estimates, Table 9 describes the simulated impacts acrossperiods. I split the study period before and after 2000. Before 2000, decrease of cold days
is more salient than increase of hot days. From 1970s (1971-1980) to 1990s (1991-2000),46

OSHA regulation states the minimum workplace temperature for workplaces, but not maximums. Onlya handful of states (e.g., California, Washington) adopt heat regulation law, which are absorbed in state-year
fixed effects. There exists no federal level heat regulation law for outdoor workers.

29

Table 9: The simulated effects of change change on prime-aged male labor force participation
rate, 1980-2019
Notes: The variables and parameters used in the simulations are described in the text. I use
periodically-variant climate estimates. The nationwide change of LFPR for prime-aged males (aged25-54) is taken from Bureau of Labor Statistics.

warming depressed LFPR by -0.04 p.p. while milder cooling augment LFPR by 0.156 p.p.
In net, climate change augmented LFPR by 0.116 p.p., accounting for 4.5% of the nationwide
LFPR drop during 1980-2000. After 2000, however, climate change has accelerated. From
1990s (1991-2000) to 2010s (2011-2019), increase in hot days on average outweigh decreasein cold days. Warming impact from additional hot days surged to 0.33 p.p. (13.2% of thenationwide drop) while cooling impacts from less cold days shrink to 0.019 p.p. Cancelling out
competing forces after 2000, climate change significantly hurt LFPR by 0.311 p.p., explaining
for 12.4% of the nationwide drop in LFPR during 2000-2019.47 . In the same period, I alsofind that climate change accounted for 16% of drop in employment-to-population ratio of
47

Operationally, I take a mean of periodic coefficients as average treatment effects (ATT) during eachperiod (e.g., effect during 1980-1990 is a mean of 1980 and 1990 coefficients). Then, multiply the periodic
ATT by change in hot and cold days across regions during the period. For nationwide LFPR data, I use
statistics for Bureau of Labor Statistics (BLS), due to potential sampling bias of Census and ACS. In
particular, 2000 Census significantly underrates labor force samples, leading to a smaller nationwide LFPR,
compared to BLS. (Lerch (2020))

30

prime-aged males.
Given the non-negligiably large magnitude, the paper conjures up a policy debate on
the heat regulation law, including a compulsory access to watering and shading, personalheat-protective equipment (cooling vests, or personal air fans) and flexible time schedule,with giving a policy implication. If labor supply response is dominant, the policy would
work to prevent further dropouts. If labor demand response is dominant, which I show by
the adverse wage response in Section 3.3, however, mandating investments might trigger
unintended consequences to shrink the employment or exits.

4.2

Migration

The empirical design of regional variations benefit from secular trend of declining internal
mobility of U.S. residents (Olney and Thompson (2024)), saliently for less-educated workers
in the U.S.. However, results might be at least partially driven by internal migration andthereby, estimates should be interpret as net effects of cross-region migration adaptations48 .
Theoretically, two modes of migration might be at work. As one scenario, workers might haveleft warming areas from increased labor cost decreased labor productivity. In this scenario,
my regional estimates could be regarded as a lower-bound. However, excluding the samples
who have cross the state border within 5 years does not significantly change the results,
indicating that migration effect is small.
Another scenario is that non-participants (e.g., retirees) moved to high-warming areas(e.g., Florida) to prefer heat as residential amenity. Given my focus on prime-aged males,
this appears to be opposite to the observed migration pattern. First, a warming trend and
LFPR are positively correlated, without controlling other correlates. Second, 80-90% of new
arrivals from other states within 5 years are labor force, suggesting that migration in theprime-aged males is chiefly driven by availability of jobs. In sum, warming areas have been
more likely to attract labor force relative to non-participants.
48

From perspective of regional policy makers, I posit that this estimates inclusive of migration response
could be more pragmatically informative, than a theoretical response in closed island model.

31

4.3

Product market channel

Previous discussions have assumed that product market is stable in response to climatechange. In line with traditional focus on agriculture of the literature (e.g., Deschênes andGreenstone (2007)), however, labor demand might shrink through product markets, especially in the heat-sensitive agriculture. Farm laborers or farmers might drop out from damaged agricultural productivity.49 Although the story is plausible in development economies,
I posit that it does not fit the wealthiest economy, the U.S., where agriculture accounts forless than 1% of the total GDP and 1.5% of employment in 2019. (only 5% of U.S. prime-aged
population in 1970). As shown in Section 2, agriculture accounts for only 20% of outdoor
workers. Excluding agricultural-intensive regions or agricultural workers from outdoor workers does not significantly change the main estimates. I conclude that the product market is
not likely to be the channel.

5Concluding remark

Throughout the human history, males have enjoyed comparative advantage in working outdoors. Guided by the secular trend of declining labor attachment of adult males, the paper
suggests that modern climate change hurt their traditional advantage. Employing a plausibly
random variation of climate change across U.S. commuting zones as a natural experiment, thepaper demonstrates that climate change impaired their labor force attachment and compensating wage differentials of working outdoors. The harm is alarmingly uneven within adultmales. Because outdoor labor market is chiefly served by noncollege workers and disadvantaged regions more intensively depends on outdoor jobs, climate change would exacerbate
both within- and cross- regional socio-economic inequality.
49

Peri and Sasahara (2019) reported urban-rural migration from climate-induced damages in agriculture
sector across the globe.

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Climate Change and Outdoor Labor Markets:
the Rise of Dropouts of Adult Males (Appendix)
Masahiro Yoshida∗
March 22, 2024

Contents
A Climate2

A.1 Regional climate change . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

2

A.2 Descriptive summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

3

B Labor market

6

B.1 Labor force participation rates . . . . . . . . . . . . . . . . . . . . . . . . . .

6

B.2 Outdoor occupations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

6

B.3 World Values Survey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

7∗
Department of Political Science and Economics, Waseda University, Tokyo
E-mail: m.yoshida@waseda.jp

1

A

ClimateA.1

Regional climate change

Weather stations Figure A.1 (left) illustrates the long-run trend of the number of weathers stations across different coverage of daily records. Figure A.1 (right) illustrates geographic
distribution of stations with complete records in 2019. As a result of entry and exits, thenumber of stations under operation largely kept increasing with some fluctuation from 1950
to 2019.

Figure A.1: Number of stations (1900-2019; left) Weather stations in the U.S. (in 2019;
right)
Note: Black points are stations with complete day available in 2019.

Population centroids Population centroids in counties are available for 2000, 2010 and2020 Census from the Census Bureau. Commuting Zone (1990) level centroids are computed
as a population-weighted average of county centroids within each CZ. County’s population
is taken from SEER (Surveillance, Epidemiology, and End Results Program) from National
Cancer Institute during 1969-2019.
Compute daily weight over weeks I compute daily temperature weight of max and mintemperature, using another Climate Normals dataset of mean hourly temperature fluctuation
during 1980-2010. First, I match a closest available station in Climate Normals to each CZ

2

Figure A.2: county- (left) vs. commuting zone- (right) level population centroids in 2020
Note: Boundaries are counties in 2020 and commuting zone in 1990.

l’s population centroid on a weekly basis. Second, I construct a daily median temperature
medianTd,lduring business hours (8 am - 6 pm), and daily maximum and minimum temperaturesminmax
at CZ l and a day d. I drop 0 am (or 12 pm), because a sizable portion of data is, Td,lTd,lmissing. Exclusion of 0 am is innocuous because 0am (or 12pm) very rarely hits the minimum
temperature either the start of the subsequent day (or end of the earlier day, respectively).
medianmax
minThird, I compute a daily weight ωd,l ∈ (0, 1) s.t. Td,l= ωd,l Td,l+ (1 − ωd,l )Td,l.
medianmin
Td,l− Td,lArranging, I obtain ωd,l =. Finally, I aggregate ωd,l to get ωweek,month,l ,
max
min
Td,l
− Td,lsplitting each month to 4 weeks: the first, second, third week consists of 8, 8, 7 days. The

forth week consists of n − 23 days, where n indexes days in each month.

A.2

Descriptive summary

Climate change statistics Figure A.3 illustrates computed levels and change of hot days
and cold days with temperature cutoff 75F and 40F, respectively.
Other climate statistics I apply the same computation algorithm of temperature toother climatological variables (humidity; precipitation; snow) recorded in Global Historical
Climatology Network daily (GHCN-daily). Figure A.4 illustrates auxiliary other climate
variable across CZs in the latest period during 2011-2019, incorporated into the empiricalmodel. precipitation and snowfall.
3

Figure A.3: Hot and cold days across U.S. commuting zones (level in 2011-2019; change from1970-2019)
Note: Thresholds of hot days and cold days are set as 75F and 40F of a median temperature during businesshours (8am - 6pm). An average of annual hot days during 1971-1980 and 2011-2019, respectively.

Humidity I use a standard meteorology formula from Glossary of Meteorology by Ameriv(Tdew )is a ratio of saturation vapor
can Meteorological Society. A relative humidity Hd ≡v(Td )
pressure under dew point Tdew and a current vapor pressure Td , where a vapor pressure v(T )is a function of daily temperature T such thatv(T ) = 0.6112 exp(17.67T /(T + 243.5))×10.
Discomfort Index is a function of business hour temperature and a relative humidity H:
Discomf ortIndex = 0.81T + H(0.99T − 14.3) + 46.3.

4

Figure A.4: Other climate variables (average in 2011-2019)
Note: Computed from GCHN-daily. The bold black line is climate regions from NOAA.

Change of hot days across climate zones Initially cool regions (Northeast, East North
Central, West North Central) indicate no significant warming trend.

5Figure A.5: Distributional shift of days across temperature bins (across NOAA climate
regions)
Note: Workhour temperature is computed as median temperature during 8am and 6pm. NOAA climate
regions are defined as follows: Northeast: CT, DE, ME, MD, MA, NH, NJ, NY, PA, RI, VT; Central: IL,
IN, KY, MO, OH, TN, WV; East North Central: IA, MI, MN, WI; West North Central: MT, NE, ND, SD,
WY; Northwest: ID, OR, WA; West: CA, NV; Southwest: AZ, CO, NM, UT; South: AR, KS, LA, MS, OK,
TX; Southeast: AL, FL, GA, NC, SC, VA.

B

Labor market

B.1

Labor force participation rates

Figure B.1 contrasts labor market participation rates of prime-aged males from 1980 to thepre-pandemic level, 2019 across commuting zones.

B.2

Outdoor occupations

Connect ONET-SOC and occupation codes in Census and ACS I connected occupation codes (ONET-SOC) in Work Context Survey in ONET to occupation codes in Census

6Figure B.1: Labor force participation rates across commuting zones (prime-aged (25-54)
males; 1980 vs. 2019)
Note: Computed from Census 1980 and ACS 2018-2019.

and ACS. occsoc codes recorded in Census 2000 and ACS since 2001 are directly connected
to occsoc2018 -based ONET-SOC identifier. I used 2018-2019 ACS samples to construct acrosswalk between occ2010 and occsoc2018 with a sample weight. occsoc2018 is connected
to occsoc2010 and occsoc2000 by a crosswalk on IPUMS.
Change in outdoor jobs across regions Figure B.2 documents share of outdoor jobs
across across CZs in 2019.

B.3

World Values Survey

Table B.1 summarizes a list of questionnaires used for empirical analysis.

7Figure B.2: Change of outdoor jobs across regional labor markets (1980-2019)

Note: (left) IPUMS of ACS 2018-2019 stacked for 2019. The outdoor worker regularly operates outdoors
at least once a week identified by a Work Context Survey from the O*NET (See the main text). (right) A
horizontal line is increase of mean number of annual hot days from 1971-1980 to 2011-2019. A vertical line
is change of labor force participation rate from 1980 to 2019, computed from Census and ACS.

8

9Source: 2017 -2018 World Values Survey Wave 7 Master Survey Questionnaire.

Table B.1: Work-related and Happiness and Well-being Questions on World Values Survey