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Climate Change and Outdoor Labor Market:
The Rise of Dropouts of Adult Males
Masahiro Yoshida

Applied Micro Workshop, Keio University
January, 2024

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Background: Global warming
Since the late 1960s, the world has experienced an unprecedented rise intemperature for the 2 millennia. In recent 5 decades in the U.S., # of hotdays (> 75F) has increased by 25.4 days per year.
• The era of global warming has ended. The era of global boiling has
arrived.” (2023 July, U.N. Secretary-General)
Figure: Global trend of temperature (left: 2 millennia; right: 1880-2020)

Source: IPCC, 2021: Summary for Policymakers. In: Climate Change 2021.

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Why care for global warming?
The climate science and env. economics demonstrated damages andadaptations with their prime focus on agriculture.
• agricultural productivity (Deschênes and Greenstone [2007]); GDPgrowth (Dell et al. [2012]); innovation (Moscona and Sastry [2022]) →What about effects on humans? (“planetary health” in COP21)• Traditional criminology documents rising crime rates (Ranson [2014]).
Recent works report adverse mental health (e.g. suicides (Burke et al.
[2018]); negative tweets (Baylis [2020]))• Extreme weather raises mortality of the elderly. (Barreca et al. [2016])• Lab studies report harms on cognitive skills. (Seppanen et al. [2003])→ What about effects on humans working outdoors?

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Fact: who works outdoors?
Historically, a consistent 20% of males (> 16 age) work outdoor regularly (atleast, weekly). Moreover, 95-98% of outdoor workers are males.
Figure: The ratio and composition of working outdoor workers by gender→ Does climate change nudge male workers to quit jobs,
and even exit from the labor market?
Note: Census, ACS, and Work Context survey at ONET. I construct an indicator where a person
regularly works outdoor (cf. at least once a week) for 873 occupations.

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Big picture: Global warming and male dropout trend
The onset of warming and rising male dropouts roughly coincided around1970. In OECD countries, LFPR peaked in 1968.
(dropouts: no work, no job search nor education)• The U.S. is most serious. (male LFPR: 96.5% (1950) → 89.1%(2019))
Figure: hot days (mean > 75F) and prime-aged male LFPRs (1960-2019, USA)

Note: LFPR from BLS. LFPR = emp. rate + unemp. rate.
The hot days are 20 year moving-average.

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Question: Did climate change induce male dropouts?
1

Outdoor workers (e.g., agriculture, construction, mining, delivery, groundmaintenance) predominantly underwent exposure to climate change.
(→ cost of work outdoors steadily increased)

2

Majority of outdoor workers are less-educated; fewer outside options inindoor jobs (e.g., office clerks; call center operators; waiters).
(← indoor jobs are more intensive in ICT and/or communication)

3

Residential air conditioners and other amenities (e.g., TV set) rapidly
spread since the 1960s.
(→ relative cost of working outdoors grew)Introduction

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Frogs in the boiled “air”?
In contrast to well-documented extreme weather effects on mortalities, theeffect is supposedly more cumulative in the long-run climate trend.
• Mental health: working outdoors brings discomfort (e.g. sweating withprotective wears)
X the rise of suicides (Burke et al. [2018]); negative tweets (Baylis [2020]).

• Physical Health: working outdoors requires standing, walking and risky
operations
X Direct effect: extra heat and dehydration causes heat-related illnesses(e.g., heat strokes; acute kidney injuries)

X Indirect effect: Lack of mental alertness causes injuries, illneses or evendisabilities. (Park et al. [2021]; leading to the opioid crisis?)

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Where does the money for dropouts come from?
Non-labor income has to be higher than the subsistence level.
• Rising living with parents (In 2021, 1/3 in adults aged 18-34).
(→ saves housing rent. U.S. houses typically have an empty kids room.)• Intrafamily transfer from parents (Binder and Bound [2019]) or spouses• Welfare benefit as a “subsidy for dropping out”
X Rise of disability insurance benefit (Parsons [1980])
X Poverty measures (e.g. social security income; food stamp)→ These fall-back options appear to be only substantial for developed
economies in the post war period.

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Why care for declining males’ labor supply?
The impact is massive because prime-aged males have traditionally been mainincome-earners.
• First-order driver of rising income inequality.
• Dropouts may lead to lower happiness (Krueger [2017]), even morbidity
and mortality. (Sullivan and Von Wachter [2009])• Higher dependency ratio threatens the social security system.
• Fewer working males plausibly lead to lower partnership and fertility
rate.1 (Autor et al. [2019])→Federal heat regulation law (currently, at only 5 states) serves as aneffective policy to prevent male dropouts.

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Strategy: “natural” experiment in the U.S. mainland.
Isolate climate effect from other confounding factors across regions.
• Variation of analysis 722 Commuting Zones (CZs) × 5 decades (1970,80, 90, 2000, 2010, 2019)• From daily (partially hourly) weather big data, I document a dramaticallyrich variation of climate change across regions.
• Identification assumption: Climate change is governed by near-random
meteorology.
X At least, an individual chooses labor supply, taking the climate as given.

• Control demographics, health, wealth, industry structure and labordemand shocks across CZs and include state×year and CZ-level FEs.
• Compliers of the treatment (i.e.; less-educated) are typically born andraised locals with less mobility. (Kennan and Walker [2011])

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Data
I assemble a panel of climates (long-run trend of daily weathers) and male
LFPRs across CZs during a half century.
• Climate change
X daily (and hourly) temperature and precipitation from 2,000-3,000
stations from GHCN-daily from National Climatic Data Center (NCDC).

X compute # of “hot days” under daily mean temperature over 75F (23.6C);“cold days” under 35F (1.7C). Using a decadal average as a climate.

X CZ most likely captures both temperature of workplaces and commuting
routes (instead of counties).

• LFPR and other controls
X micro data; Census (1-5% of the universe; 1970-2000, by decades), andAmerican Community Survey (2010-2012, 2017-2019)

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Preview of main findings• 10 more hot days (>75F (23.6 C)) throughout a decade decreased male
LFPR by 1.0 p.p. or generated 200,000 prime-aged male dropouts.
• Climate change accounts for approximately 5-10% of dropouts
during 1970-2019.
• The effect is larger for younger males, leading to aging of outdoor
workers.
• Climate change reduced employment rate of outdoor jobs, buttransition to indoor jobs is seemingly limited.
• Climate change hurt real wages for survivors, shrinking thecompensating wage differentials of working outdoors.

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Literature: closest papers on temperature-labor link
Recent economists explore natural experiments in the field.
• labor productivity (partly absenteeism)
manufacturing in India (Somanathan et al. [2021], JPE); China (Chen andYang [2019], JEEM)• time allocation: work time & outdoor leisure↓ and indoor leisure↑(Graff Zivin and Neidell [2014], JLE)

→ All explores daily reactions from extreme weathers.
• labor demand response: small manufacturing plants exit (Ponticelli
et al. [2023]); employment cut (Acharya et al. [2023])→ My paper addresses labor supply shift from climate change.

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Literature: males’ labor supply
The paper contributes to the literature on male labor supply in rich countries.
None spans long enough to reconcile the half-century global phenomenon.
• Labor demand drivers: computerization (1990s); robots (1980s?);(Acemoglu and Restrepo [2020]); China shock (2001-) (Autor et al.
[2014]) and offshoring (1980s?)• Labor supply drivers: disability insurance (Parsons [1980]); health(Krueger [2017]); computer game technology (Aguiar et al. [2021])(Arguably) the first paper to directly test the effect of measurable cost ofwork on labor supply.

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Model: Working outdoors or dropout• Consider a basic labor supply model where a market wage w(s) for skills ∈ [0, 1], non-labor income NLI and unobservable e are given.
• Choose “dropout” or “work outdoors” or “work indoors”U drop = NLI
work ( s ) = w
Uoutout ( s ) − cout ( hd, cd ) − e





U work (s) = win (s) − cin − ein(dropout)(outdoor)(indoor)0 ( s ) < w0 ( s )).
• Assume indoor jobs are skill intensive (woutinIntroduction

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Propositions: dropouts from outdoors
As climate gets severer, a person with skill s is more likely to dropout.
1

The mean skill level s for outdoor workers increases as positive selection.

2

0 ( s )  w0 ( s )),If indoor jobs are sufficiently differentiated (woutinchange to working indoors is limited.
Figure: Occupation choice (when e = 0)

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Propositions
work ( s ) > U drop , the person works.
• Under some weak regularity, if Uout

Then, LFPR is computed by summing up e s.t.
LFPR =Z ∆0f (e)dewhich is strictly increasing in the relative benefit of working outdoorswork∆ ≡ Uout(s) − U drop = wout (s) − ( NLI + cout (hd, cd) + e)
• A person is more likely to drop out if one of the 3 effects is salient.
X Substitution effect: wout (s) ↓ (adverse labor demand shock)
X Income effect: NLI ↑ (family income or public welfare)
X Climate effect: hd or cd ↑Introduction

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Eqm: Labor market of Outdoors
The employment rate of outdoor jobs shrinks. The impact of wage isambiguous, depending on relative shifts and curves of labor supply anddemand curves.
Figure: Outdoor Labor market

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Daily weather big data
Daily weather (max & min temperature and precipitation) big data comesfrom 2,000-3,000 stations, recorded by GHCN-Daily.
• I take an inverse-distance weighted average of 10 closest station
records within 100km from CZ popu. centroids.
Figure: stations recording temperature (1900-2019); distribution (2019)

Source: GHCN-Daily from NOAA (National Oceanic and Atmospheric Administration).

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Construct a daily mean temperature
I compute a daily mean temperature as Td = ωTmax + (1 − ω ) Tmin .
1

Convention of literature: ω = 0.5. (underestimates the temperature
faced by workers, especially in the summer.)2l
Biz hour (8AM-6PM) temperature: ωweek,month
I compute a weekly

CZ-specific ω by mean hourly temperature data during 1981-2010 across 408
stations (Climate Normals dataset).
Figure: Distribution of weekly temperature weight ω across seasons

Note: Average in a single week in 4 weeks within each month recorded by one station.

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Hot days across regions
In the continental U.S., significant regional variation of climate change, even
within states. Initially hot south areas have been increasingly getting
hotter. Some regions experienced cooling.
(→ Different shocks from automation, ICT or China shocks!)
Figure: hot days (mean > 75F) across CZs (level, 2010-19 (left); change from
1960-70 to 2010-19 (right))

Source: Computed from GHCN-daily.

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LFPR of prime-aged males
Male LFPR significantly dropped, especially in the Southeast and South. Over
90% of prime-aged male dropouts do not have college degrees.
Figure: LFPR of males across Commuting Zones (1970 vs. 2019)

Note: Bold line is a climate zone from NOAA.

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Which outdoor jobs are largest?
Majority of outdoor occupations are in the primary sector. (especially,
agriculture and construction, but not all)Table: Occupation rankings of outdoor workers

Note: occupation rankings are only occupations with 1 million employment in 2019, computed fromIPUMS of ACS, 2017-2019 (for 2019). The score is an average of respondents by each occupationfrom Work Context Survey.

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Fact: outdoor workers by sectors
Agriculture and construction accounts for the half. Transportation and service
sectors are the second largest share.
Figure: The ratio and composition of outdoor workers by sectors

Note: Computed from IPUMS of Census and ACS. The outdoor worker regularly operates outdoors
at least once a week identified by a Work Context Survey from the O*NET.

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Fact: who works outdoors?
Increasing fraction of non-colleged workers work outdoors.
Over 90% of outdoor workers do NOT have college degrees.
Figure: The ratio and composition of working outdoors by education

Note: Computed from IPUMS of Census and ACS. The outdoor worker regularly operates outdoors
at least once a week identified by a Work Context Survey from the O*NET.

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Fact: characters of outdoor jobs?
Outdoor jobs are physically demanding and risky. (→ sources of injuries
and illness) However, outdoor jobs are non-routine and hard to automate
despite its needs. (→ labor demand for “essential” workers is inelastic)
Table: Estimates from other climate proxies

Note: Work Context Survey by ONET. ∗ ∗ ∗ : p < 1%; ∗∗ : p < 5%;∗ : p < 10%.

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Empirical model: isolate climate effects
I estimate a DID-style model by a panel data regression at CZ l and decadesd = (d, d] ∈ {(1960, 1970], (1970, 1980], · · · , (2010, 2019]:
LFPRl,d =Σbin βbin dayl,d|{z}+ Xl,d|{z}+δs,d + δl| {z }

10 bin form of climate change

+

Bl,d
|{z}

labor demand shocks

covariates

state×decade FE and CZ FE

+

el,d
|{z}

clustered by CZs• Regional covariates Xl,d at start-of-decade t includes:
X Other climate proxies: precipitation, snowfall
X Demography and family: age, education, race and ethnicity, veterans,senior ratio (> 65), marital status, children

X Industry structure: Industry mix, unemployment rate, female LFPR
X Health: disability Wealth: personal or family NLI, housing value, poverty
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Labor demand shocks
To incorporate labor demand shocks Bl,d , I construct a shift-share Bartik
shocks for both employments and wages of prime-aged workers, following
Goldsmith-Pinkham et al. [2020].
Bl,d = ∑ksl,k,t0
|{z}

gk,t
|{z}

share of l’s employment in industry k national industry k growth rate

• This includes technology revolution (ICT; automation) and free
trade shocks.

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Flexible bin estimates (males; 1970-2019)
I use 75F (23.6 C) and 35F (1.7C) as thresholds for hot days and cold days.
Table: Climate impact on male LFPRs (across CZs; a benchmark is 70-75F.)

Note: red dashed lines are 95% CI. Distribution of days across temperature bins during each decade.

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Baseline model
To improve precision, I simplify bins to hot and cold days, possibly interactedwith start-of-period outdoor intensity.

∑

LFPRl,d =βz (dayl,d )z ×z∈{ hot,cold}

|

{z

}

RatioOutdoorl,d|{z}start-of-decade outdoor intensity+ Xl,d|{z}

covariates

limited to hot and cold days

+

Bl,d
|{z}

labor demand shocks+

δs,d + δl| {z }

state×decade FE and CZ FE

+

el,d
|{z}

clustered by CZs

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Baseline result (males; 1970-2019)
I find 10 more hot days reduces LFPR of outdoor workers by 0.99 p.p.
during 1970-2019.
Table: Climate impact on male LFPRs (across CZs)

Note: ∗ ∗ ∗∗ : p < 0.1%; ∗ ∗ ∗ : p < 1%; ∗∗ : p < 5%;∗ : p < 10%.
Weighted by a start-of-period share of prime-aged male population. SE is clustered by states.

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Baseline result (females; 1970-2019)
Under the same specifications for females, the effects are negatively large
with less precision. (← Outdoor workers are predominantly males.)Table: Climate impact on female LFPRs (across CZs)

Note: Weighted by a start-of-period share of prime-aged female population. SE is clustered by states.

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By education: Less-educated reacted more
Consistently with the model, the effect is larger for the less-educated. Using
discomfort index (including humidity) and limit to non-rainy days
strengthens the estimate.
Table: Effects by education

Note: ∗ ∗ ∗∗ : p < 0.1%; ∗ ∗ ∗ : p < 1%; ∗∗ : p < 5%;∗ : p < 10%. Full-controlled and FEs in state ×
year and CZs. Weighted by a start-of-period share of prime-aged male population.

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By age: younger people reacted more
The effect is largest for younger adults (25-34). (→Alarming for humancapital accumulation and in marriage and partnership.)Table: Effects by age

Note: ∗ ∗ ∗∗ : p < 0.1%; ∗ ∗ ∗ : p < 1%; ∗∗ : p < 5%;∗ : p < 10%. Full-controlled and FEs in state ×
year and CZs. Weighted by a start-of-period share of prime-aged male population.

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Net effects by climate change
Summing up increase of hot days and decrease of cold days, the climatechange explains approximately 5-10% (0.24-0.48M headcounts) ofincreased prime-aged male dropouts during 1970-2019.
Figure: Climate change and regional effects

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Employment rate drops
Climate change reduces males’ salaried employment rate rate. (increased
the self-employed, especially working at home)
Table: decomposition of LFPR

Note:∗ ∗ ∗∗ : p < 0.1%; ∗ ∗ ∗ : p < 1%; ∗∗ : p < 5%;∗ : p < 10%. Full-controlled and FEs in state×year and CZs. Weighted by a start-of-period share of prime-aged male population. SEs are clusteredIntroduction

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Outdoor to indoor?
Seemingly, no. No clear evidence for rise of indoor workers.
• Sunlight is not an exclusive factor. (See outdoor with cover)• Commuting potentially affects all workers. (See closed vehicle)
Table: Outdoor vs. indoor vs. dropouts

Note:∗ ∗ ∗ : p < 1%; ∗∗ : p < 5%;∗ : p < 10%. Full-controlled and FEs in year×CZs.
Weighted by start-of-period share of prime-aged male population. SEs are clustered by CZs.

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Testing the occupational transfer
Climate change increased the education level of outdoor workers (←consistent with negative selection of dropouts) and fueled aging of outdoor
workers (←Young workers are less likely to work outdoors))
Table: Climate effects on education and age composition

Note: ∗ ∗ ∗∗ : p < 0.1%; ∗ ∗ ∗ : p < 1%; ∗∗ : p < 5%;∗ : p < 10%. Full-controlled and FEs inyear×CZs. Weighted by start-of-period share of prime-aged male population. SEs are clustered bystates.

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Wages: by regional level
More hot days increases the gross wage of outdoor workers. (← negative
selection of dropouts and aging of outdoor workers)
Table: Climate effects on gross wages (by regions)

Note: ∗ ∗ ∗ : p < 1%; ∗∗ : p < 5%;∗ : p < 10%. Full-controlled and FEs in year and CZs.
Weighted by a start-of-period share of prime-aged male population.

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Wages: by worker level
Adjusting demographics, more hot days hurt the wage of outdoor workers.
(→shrink the compensating wage differentials of outdoor workers)
Table: Climate effects on adjusted wages (by worker)

Note: ∗ ∗ ∗∗ : p < 0.1%; ∗ ∗ ∗ : p < 1%; ∗∗ : p < 5%;∗ : p < 10%. CZ* Occ group FE, state * year
FE, Occ group * year FE are included. Weighted by a period share of person weight.

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Alternative stories
1. Mobility of workers (e.g., Peri and Sasahara [2019])• workers might’ ve left warming areas.
X Excluding the state movers in recent 5 years enlarge the estimates.

• early-retirees might’ ve moved to warming areas (e.g. Florida).
X warming areas attracted more labor force than non-labor force.

2. Product market channel• Climate change hurt agriculture thus, suppresses incomes at farms.
X Agriculture accounts for less than 1% of GDP and 1.5% of employment in
the U.S. (leading developed economy)

X Excluding agriculture-intensive regions does not affect the estimates.

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Summary: Climate meets labor markets
Throughout the human history, males have enjoyed comparative advantage inworking outdoors. The paper suggests that modern climate change hurt
their traditional advantage.
• Climate change exacerbates the economic inequality among males.
X outdoor jobs as hotbeds for dropouts
X Hot days hurt wages of outdoor jobs, after controlling for survivors’
composition.

• Policy implication: federal-level heat regulation (shading; water; airconditioner) would prevent further dropouts.

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For comments
Thank you for listening. Please feel free to send me a feedback to my email:m.yoshida@waseda.jp

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AppendixDefinition: Climate region
The U.S. includes 9 climate regions from tropical one to deserts.
Table: Annual hot days by climate regions (1970-2019)

Source: National Centers for Environmental Information (NCEI); NOAA

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Climate change by climate region
When climate is measured by hot days, hot regions are increasingly getting
hotter.
Table: Annual hot days across climate regions (1970-2019)

Source: Computed from GHCN-daily.

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Humidity
Humidity is lower in the west area close to mountains and higher in the eastarea near the Mexican gulf.
• Daily relative humidity is computed from a dew point and mean
temperature through a standard meteorological formula.
Figure: Humidity across CZs (2019)

Source: Computed from Global Summary of the Day (GSoD).

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Examples: Outlier cities
New Orleans in Louisiana is much more humid than Phenix in Arizona withcomparable temperature. (→ discomfort in summer is much different.)
Figure: Distribution of mean daily temperature and humidity (2002 only)

Source: Barreca (2012); Humidity is a specific humidity (g/kg).

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Precipitation
Humidity is heavily shaped by precipitation. East region has larger annual
precipitation and rainy days due to the U.S. geography. West region neardesserts (especially, California) suffers from droughts and even forest fires.
Figure: Precipitation across CZs (average in 2000-2019)

Source: Computed from GHCN-daily.

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Spread of Air conditioner
Hotter regions experience faster adoption rates of ACs. (Biddle [2008])(→ the relative cost expanded even more quickly in hot areas.
Figure: Fraction of households with residential ACs (1960-1980)

Note: Census of Households, 1970 and 1980. Extrapolation by an author based on Barreca
et al. [2016].

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Do workers avoid discomfort?
DI is a function of temperature and relative humidity. Uncomfortable days
have DI > 75. (A majority of people feels uncomfortable)• The West or Southwest regions are comparably hot, but lessuncomfortable compared to the Southeast or South.
Figure: Discomfort index across CZs

Source: Computed from GHCN-daily.

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Stylized fact: LFPR trend by education (US)The dropout is stark for the less-educated.
(→Many literature explores the adverse labor demand shocks)
Figure: LFPR by education attainment (males aged 25-54)

5_graphics/wage_2019JEP.png
5_graphics/lfpr_2019JEP.png
Note: BLS, NBER. (Source: Binder and Bound [2019])

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Regional dependency on outdoor jobs
Poorer regions rely more on outdoor jobs.
Figure: Share of outdoor jobs across regional labor markets (2019)

Note: IPUMS of ACS 2017-2019 (for 2019). The outdoor worker regularly operates outdoors atleast once a week identified by a Work Context Survey from the O*NET.

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References

Daron Acemoglu and Pascual Restrepo. Robots and jobs: Evidence from us
labor markets. Journal of Political Economy, 128(6):2188–2244, 2020.
Viral V Acharya, Abhishek Bhardwaj, and Tuomas Tomunen. Do firms
mitigate climate impact on employment? evidence from us heat shocks.
Working Paper 31967, National Bureau of Economic Research, December
2023.
Mark Aguiar, Mark Bils, Kerwin Kofi Charles, and Erik Hurst. Leisure luxuries
and the labor supply of young men. Journal of Political Economy, 129(2):337–382, 2021.
David Autor, David Dorn, Gordon Hanson, et al. When work disappears:
Manufacturing decline and the falling marriage market value of young men.
American Economic Review: Insights, 1(2):161–78, 2019.

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