The Impact of Climate Change on Work:
Lessons for Developing Countries
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Moustafa Feriga, Nancy Lozano Gracia , and Pieter Serneels
We identify five areas where climate change may impact work and draw lessons for devel-
oping countries by reviewing the evidence. Firstly, demand for labor is unevenly affected,
with agriculture, heat-exposed manufacturing, and the brown energy sector experiencing
downturns, while other sectors may see a rise, resulting in an uncertain overall impact.
Secondly, climate change impacts labor supply through absenteeism, shirking, and alter-
ing work-time patterns, depending on the activity and sector. Thirdly, productivity may
decline, especially in heat-exposed industries, primarily due to health reasons. Fourthly,
heightened earnings variability likely increases vulnerability among the self-employed.
Fifthly, climate change can influence labor allocation and catalyze sectoral reallocation.
Higher temperatures are also linked to increased migration. But caution is needed in inter-
preting these findings, as studies across these topics predominantly use fixed effect estima-
tion and concentrate on short-term impacts, neglecting adaptation. Emerging research on
adaptation indicates that workplace cooling is unappealing for firms with narrow profit
margins, while coping strategies of farms and households have unclear optimality due to
adoption barriers. Government responses remain understudied, with six potential areas
identified: green jobs, green skills, labor-oriented adaptation, flexible work regulation, la-
bor market integration, and social protection. We conclude by outlining future research
directions.
JEL Codes: Q54, J01, O1
Keywords: climate change, labor, development.
Introduction
Work is the main source of income for most people around the world, and in particu-
lar for the poor. As a primary factor of production, labor also plays a key role for eco-
nomic growth. Yet our understanding of how climate change impacts labor remains
The World Bank Research Observer
© 2024 International Bank for Reconstruction and Development / The World Bank. Published by Oxford University Press
https://doi.org/10.1093/wbro/lkae002 40:104–146
fragmented. This paper takes stock of what we know, distinguishing five ways in which
climate change may affect labor. We discuss limitations and challenges with existing
estimates, underlining the need for careful interpretation. A central issue of concern is
the omission of adaptation, and we discuss recent insights on the responses by firms,
farms, households, and workers. Together, the evidence suggests several potential ar-
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eas of government response. We discuss possible labor policies and ways forward for
research.
A growing literature establishes the impact of climate change on general economic
performance. Weather variation—measured in terms of temperature, precipitation, or
climate change-related weather events—is estimated to lower economic output, with
largest expected impacts for low- and middle-income countries.1 Changes in weather
also increase or deepen poverty, in particular in rural and exposed areas, and among
vulnerable groups.2
Labor represents a possible key channel through which climate change affects eco-
nomic performance and poverty. Reviewing the most rigorous existing evidence, we
distinguish five potential areas for the impact of climate change on labor. Immedi-
ate impacts manifest in: labor demand, labor supply and time allocation, on-the-job
productivity, and income and vulnerability among the self-employed. In the medium-
term, climate change may lead to a reallocation of labor across economic activities and
across space.
The studies we consider follow a common estimation strategy, with impact esti-
mates typically obtained through fixed effect estimation. These estimates generally
capture the short-term, immediate effects of past events and do not account for po-
tential adaptation. They might therefore overestimate long-term effects, in particular
if substantial adaptation is expected. Conversely, they might underestimate long-term
impacts, when they fail to consider effects across the economy, or if climate change
worsens, i.e., realized past events are a poor predictor for future occurrences. While
we mention research aimed at tackling the latter two concerns, mainly through gen-
eral equilibrium analysis and simulation of future outcomes, where appropriate, our
primary interest lies in the emerging adaptation research that examines responses by
firms, farms, households, and workers.
Keeping in mind these potential shortcomings, several key findings emerge. The im-
pact on labor demand varies across sectors, with negative effects observed in some
and positive outcomes in others, resulting in an overall uncertain impact. Agricultural
employment experiences a significant downturn. The manufacturing sector witnesses
substantial decreases in output, particularly in heat-exposed industries, but the link-
age with changes in employment remain inadequately explored. As energy undergoes a
transition, employment in brown energy sectors diminishes while green energy sectors
see a rise. The service sector, including transportation, remains insufficiently studied.
More in-depth analyses specific to each sector at the country level are required to im-
prove our understanding of the overall impacts on employment.
Feriga, Lozano Gracia, and Serneels 105
On the supply side, studies reveal potential impacts on absenteeism, shirking, and
time patterns of work, dependent on the activity and sector. Productivity experiences
pronounced negative impacts, particularly in heat-exposed industries, extending to in-
door activities. Health emerges as a critical channel in this context, prompting the need
for further investigation. For the self-employed, heightened variability in earnings am-
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plifies their vulnerability.
How do these changes impact the allocation of labor? The decline in agricultural
employment and productivity act as a catalyst for sectoral reallocation, possibly accel-
erating structural transformation. The limited evidence available suggests that flexi-
ble and integrated labor markets can facilitate this transition. Rising temperatures are
also associated with increased migration, both within and across countries, and in-
volving permanent relocation, unlike rainfall, for which the effects vary and are not
consistently observed. But identifying and attributing causation remains a challenge.
Migration continues to be a universal coping strategy to navigate income fluctuations,
regardless of their source, and climate change is one factor that may contribute to in-
creased variability.
While most analyses do not account for adaptation, some explicitly examine the re-
sponses to climate change by firms, farms, and households. These studies analyze the
extent of adaptation, how it moderates the impact of climate change, or compare short
and long-term responses. Research evaluating firm responses predominantly concen-
trates on the adoption of workplace cooling, which seems generally unappealing for
firms with low profit margins due its high cost. Recent work finds early evidence for
firm relocation as a potential strategy. Farms, on the other hand, are observed to em-
ploy various strategies to cope with climate change, including irrigation, fertilizer use,
change in seed and crop choice, or planting of trees, among others. It remains unclear
whether these adaptation strategies are optimal, as there are evident barriers to their
adoption. In a similar vein, rural households adopt multiple risk-coping strategies to
reduce their exposure to climate-induce income shocks, such as consumption reduc-
tion, use of savings, and borrowing. These strategies fall short of optimal as they do not
provide complete insurance against income loss.
This leads us to the question how governments can, and do, respond. There has
been limited research investigating the impact of general climate change policies on
labor outcomes, highlighting the need for more thorough examination. In the realm of
labor-targeting climate change policies, we distinguish six potential areas where gov-
ernments can take action: green jobs, green skills, labor-oriented adaptation, flexible
work regulation, labor market integration, and social protection.
Our review demonstrates the impact of climate change on labor to be a growing
area of research, where much is to be discovered. We conclude by setting out avenues
for future research.
We review the evidence, starting with a note on methods used and challenges
of identification, before distinguishing the five ways in which climate change may
106 The World Bank Research Observer, vol. 40, no. 1 (2025)
impact labor. We then continue with a discussion on how agents respond, commenc-
ing with an examination of analytical methods, followed by a discussion of how firms,
farms, households, and workers respond. A subsequent section examines government
response. The concluding section discusses ways forward for research and the evalua-
tion of policies in this field.
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The Impact of Climate Change on Labor
Methods and Identification
This overview focuses on studies that estimate the impact of climate change on labor
outcomes using regression analysis, typically taking the following form:
jt + Ci jt + Xi jt
Yi jt = α + β Ti e e
+ μi + μ j + γ jt + εi jt (1)
Where Yi jt is the labor variable of interest, and β is the impact of climate change—
often temperature—in area j at time t, to which worker i (in micro studies) or sector or
country i (in meso or macro studies) is exposed (Ti e jt ). Some papers look beyond tem-
perature, at precipitation, or other climate indicators. Ciejt reflects other climate change
events that may be included or not in the regression. Xi jt are individual worker (or sec-
tor or country) characteristics that affect Yi jt . μi and μ j are unobserved individual and
location fixed effects respectively (which are combined in some studies), and γ jt is a
time trend.3 This estimation typically combines longitudinal data on economic out-
comes4 with climate data from weather station, gridded, satellite, or other secondary
sources and exploits short-run, plausibly exogeneous variation in climate to estimate
impact on labor outcomes of interest.
Studies may differ in the variables they include on the right-hand side. Research fo-
cusing on temperature typically controls for other climate events like precipitation.
The observed subject characteristics that are included as control variables may also
vary. Including fixed effects and time trends yields a more robust estimate. Note that
while the studies under review generally employ the outlined estimation strategy, a few
instances deviate from this approach by not including a comprehensive set of controls,
individual or location fixed effects. Consequently, these analyses yield more descriptive
findings. We have retained such research only in cases where alternative information
is scarce or when they provide particular pertinent insights, clearly indicating the de-
scriptive nature of their findings. Additionally, a small number of studies leverage dras-
tic changes in climate, such as extreme drought or tsunami, as natural experiments,
or use instrumental variable (IV) estimation, or in rare occasions employ randomiza-
tion, achieving more rigorous causal estimates, which we clarify when we discuss the
paper.
The next sections zoom in on specific left-hand side labor variables of interest, one
at a time: demand, supply, productivity, income, allocation, and migration. The studies
Feriga, Lozano Gracia, and Serneels 107
on allocation compare estimates from across different sectors. Throughout the estima-
tions, challenges remain in obtaining estimates of β that reflect causal impact, given
the non-experimental nature of the data. We highlight six issues of potential concern.
First, the temperature (or other climate event) to which workers are actually exposed
remains unobserved. Locally measured temperature (or climate change event) serves
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as a proxy.5 This will affect precision, and can lead to biased estimation, in particular
if the exposed temperature is very different from the observed one.6 In general, studies
seem to be aware of this concern and pay attention to avoid major mismeasurement
of this nature.
A second limitation is that the studies estimate historic and short-term, immedi-
ate, impacts. Firms, workers, markets, and regulators may adapt to higher tempera-
ture and other climatic changes, possibly reducing their impact on labor. The extent
to which adaptation takes place is a topic of increasing attention and the subject of
a nascent literature, which we discuss in the section on response. In general, our un-
derstanding in this area remains limited. Adaptation will also be heterogenous in form
and in the time it takes to respond. In some countries (low income), areas (rural), and
sectors (agriculture, outdoor), adaptation is likely to be slower. Nevertheless, because
accounting for adaptation remains a challenge, most of today’s estimates need to be
seen as short-term impacts. They may be an upper bound if there is increased adapta-
tion in the future; they may be a lower bound if changes in climate become more severe
and lead to higher impacts.
While some research discusses potential omitted variables, other studies do not.
Yet in the absence of experimental data, estimates may be biased due to omitted vari-
ables. One key unobservable of high importance is the capacity for adaptation. Firms or
workers with some characteristics—e.g., more profitable, wealthier—may be quicker at
taking adaptive measures, thereby reducing impact. Other omitted variables, including
climate variables, may also matter.7
Fourth, the estimations typically include location fixed effects, which help to ob-
tain a more robust impact estimate, but do not shed light on the salient aspects of
context. Which characteristics of the local economy and labor market matter most, is
important for policy design. One way to address this is to carry out subsample analy-
sis, breaking down the results for different subgroups or localities along pre-meditated
dimensions of interest. Another approach is to focus a study on specific settings, ex-
plicitly taking into account salient characteristics, leading to richer interpretation, and
highlighting gaps in our understanding.8
Fifth, to address the uncertainty about future trends, a separate strand of papers
looks at projections for the future, using simulation-based approaches starting from
predicted temperature and current economic outcomes. They use a distinct type of
analysis from the one set out above. Their inference is informative, providing a sense
of the general trends, albeit under specific assumptions (Stern 2008). These estimates
are not the focus of attention in this paper, although we include the results from a few
108 The World Bank Research Observer, vol. 40, no. 1 (2025)
overview studies because of their informative nature—we mention these explicitly as
simulation studies.
A sixth concern is that the estimates are typically obtained for partial equilibrium.
General equilibrium (GE) effects—changes across the economy that come about from
linkages between economic activities—are neglected. Specific studies try to shed light
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on GE effects using distinct methodologies, including Computable General Equilib-
rium (CGE) and Dynamic General Equilibrium (DGE) models or Input-Output (I/O)
analysis. While these studies are useful to get some idea of data patterns, their results
are quite sensitive to the underlying assumptions.9 We mention these studies where
informative, although they are not central to our overview.
Despite these limitations, the estimated impacts are the best we have for now. Com-
parison across the most rigorous studies can help assess the robustness of these esti-
mates. Nevertheless, critical assessment and meticulous interpretation remain key, as
discussed throughout the next sections.
Labor Demand
Climate change is expected to affect the demand for labor through diverse channels.
Where climate change reduces output, as the evidence suggests for specific sectors, in-
cluding agriculture, derived demand for labor will typically go down. At the same time,
as the world moves toward greener production, demand for labor in carbon-intensive
activities, like nonrenewable energy, is anticipated to decline, while that in green ac-
tivities is set to increase. As it stands, we possess an incomplete comprehension of the
net effect of climate change on labor demand. To gain a clearer understanding, sectoral
analysis is needed. In what follows we provide a summary of expected climate impacts
on labor demand in selected sectors.
Agriculture
Agricultural output can be affected in several ways. Growing evidence documents how
increased heat reduces agricultural output in low- and middle-income countries, vary-
ing between 5 percent and 25 percent, depending on the crop, region, and time span
considered.10 Increased rainfall is often positively related to agricultural output.11 Ex-
posure to cyclones bears negative impacts.12
Direct impacts on the demand for labor have been observed across several devel-
oping country settings. In Mexico extreme heat is associated with a 1.4 percent de-
cline in rural employment over a 28-year period, driven by weather-impacted agri-
cultural yield losses. Reductions are largest for non-farm labor, as a result of the re-
duced demand for non-agricultural goods (Jessoe et al. 2018).13 In El Salvador total
corn production declined by as much as 2.8 percent for an additional week of high
Feriga, Lozano Gracia, and Serneels 109
temperature during the harvest season (Ibanez et al. 2022). Agricultural producers ad-
justed by using more household labor and less hired workers, who either migrate or
change to a non-agricultural job. In Brazil a 1-standard deviation increase in excess
aridity reduced planted and harvested areas by 1.6 percent and 2.7 percent, respec-
tively, during 2000–2010. Agricultural employment fell by around 11 percent, in line
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with the negative impact on agriculture output (Albert et al. 2021). In China, a 1◦ C in-
crease in temperature is associated with a 7 percent reduction in farm work across
rural communities (Huang et al. 2020).
Reduced demand for labor may especially affect women. In India, after a drought
shock, women were 7 percent less likely to be in employment compared to men (1.2
percentage points for women versus 0.5 percentage points for men), they spend 29 per-
cent more days seeking work, and have 20 percent lower non-farm workdays; those in
farm work see their real earnings fall by 38 percent compared to 3.4 percent for men
(Afridi et al. 2022).
Most studies in agriculture suffer from at least two of the limitations mentioned in
the previous section. First, they abstract from adaptation behavior of farmers, which
is likely to temper long term impacts. A handful of studies account for adaptation and
consider multiple seasons, but the findings are disparate. Chinese farmers use more
machines to compensate for the shortage of labor arising from extreme hot weather,
and this offsets nearly 47 percent of the short-run effects on agriculture yield over a 35-
year period (Chen and Gong 2021). One attractive method to study adaptation exists
of comparing short-term panel estimates to long-term difference estimates. Exploit-
ing large variation in weather across time and counties in the United States, one study
finds minimal long-run adaptation in terms of unchanged agricultural inputs and stay-
ing with the same crop, while the cultivated area declined (Burke and Emerick 2016).
The limited adaptation is understood to arise from farmers either not being fully aware
of a changing climate, or recognizing the need for adaptation but being unable to im-
plement adjustment. A second shortcoming is that these studies rarely look at labor
outcomes beyond agriculture, and in doing so, ignore labor mobility, including part-
time work elsewhere, as a margin of adjustment. When labor is mobile (across sectors
or geographies) long-run impacts may be considerably smaller compared to the esti-
mated short-run effects. The section on reallocation of labor below zooms in on labor
mobility.
Industry
Climate impacts on non-agricultural output tend to be more pronounced for develop-
ing countries, and stem primarily from hot weather and extreme heat. Overall, indus-
trial value-added is found to shrink by 2 percent for every 1◦ C warming, but only in
low-income countries (Hsiang 2010; Dell et al. 2012). Precipitation effects are less ro-
bust, though there is variation by region (Dell et al. 2012). These impacts seem to affect
110 The World Bank Research Observer, vol. 40, no. 1 (2025)
industrial exports from low-income to high-income economies, which decline by 2.4
percent for a 1◦ C temperature rise but do not vary with changes in precipitation (Jones
and Olken 2010).
Employment in heat-exposed industries has received special attention in the litera-
ture. Significant impacts of heat on the number of workers employed by manufactur-
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ing firms are evident in the United States, with small plants in counties witnessing a
more substantial rise in long-run average temperatures experiencing a decline of up
to 6 percent (Ponticelli et al. 2023). The few studies for developing countries typically
concentrate on output, rather than employment, and find that output elasticity of la-
bor is a primary mechanism for the observed negative effects of higher temperature.
In India a 1◦ C warming in daily temperature depresses annual firm output by 2.1 per-
cent across manufacturing nationwide over 15 years; this seems to be driven by reduc-
tions in the output elasticity of labor, rather than capital or other factors of production
(Somanathan et al. 2021). Firm-level evidence from China confirms high temperature
to affect sectoral output due to heat exposure: an additional hot day (< 90◦ F, 32◦ C)
negatively affects output of almost half of the two-digit industrial sectors in China, in-
cluding both labor-intensive and capital-intensive sectors, with notable heterogeneity
in the magnitude of impact. For example, in timber manufacturing, output is expected
to fall by 1.26 percent due to an additional hot day, while in nonmetal mining it would
reduce by 1 percent. Impact is insignificant in medicine manufacturing and smelting of
nonferrous metals, among others. High-tech industry output is insensitive to tempera-
tures between 80–90◦ F (27–32◦ C) but sensitive to increase beyond that, while low-tech
industry output is negatively impacted above any of these temperatures. These effects
are large in economic terms: when assuming no additional adaptation, Chinese man-
ufacturing output would be expected to fall by 12 percent annually (Zhang et al. 2018).
These findings are in line with in-depth research for the United States on employment
loss in heat-exposed industries (Behrer and Park 2017).14
The impact of cyclones on industrial output varies by sector and over time. In the
Caribbean, one standard deviation increase in the exposure to cyclones is negatively
related to output in mining and utilities (-0.9 percent), but positively related to con-
struction after the event (+1.4 percent) (Hsiang 2010). In India,15 these events reduced
3 percent of firm sales, destroyed 2 percent of firm assets, but had no impact on salaries
or labor inputs across a panel of manufacturing firms between 1995 and 2006. Overall,
these impacts tend to be short-lived and disappear after a year, with better-performing
industries recovering faster (Pelli et al. 2023).
As with the research on agriculture, the above studies have two important caveats.
First, they tend to concentrate on immediate, short-term impact, neglecting potential
adaptation. Second, they typically do not account for impacts outside the sector, which
may be considerable.
Feriga, Lozano Gracia, and Serneels 111
Energy
Demand for energy is expected to increase with climate change, reflecting con-
sumers’ short-term response and long-term adaptation.16 In Mexico, each additional
day < 90◦ F (32◦ C) increases monthly electricity consumption by 3.2 percent. As in-
comes rise, air conditioning (AC) ownership increases with temperature by 3 and 27
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percentage points, in cool and warm places, respectively, for a $10,000 increase in an-
nual household income (Davis and Gertler 2015).17
Most research on the energy sector concentrates on the expected impact of the shift
towards green energy, following the global move towards low carbon, rather than the
impact of climate change events themselves. Replacing fossil fuel with renewable en-
ergy will lead to job destruction in carbon-intensive industries and job creation in low-
carbon sectors.18 Existing evidence suggests a positive net employment effect in the
energy sector for this climate transition. Because changes in energy have immediate
effects across multiple sectors, many of these studies take a sector-wide approach and
focus on simulation of future outcomes.
An ex-ante I/O simulation indicates that if 139 countries were to convert 100 per-
cent to clean energy from wind, water, and solar by 2050, 27.7 million jobs could be
lost in high-carbon sectors such as oil, gas, coal, and biofuel, while approximately 52
million full-time jobs could be created (25.4 million construction jobs and 26.6 million
operation and maintenance jobs), resulting in a net gain of 24.3 million full-time jobs
in the sector (Jacobson et al. 2017). Another study simulates the direct employment
capability of power plants across the world and finds that renewables can generate be-
tween 0.1 to 4 job-years per GWh on average versus 0.1 to 2.4 job-years per GWh for
non-renewable power plants (Barros et al. 2017).
A regional CGE model for South Africa that analyzes the impact of a smaller share
of coal in the energy supply mix finds an anticipated decline in employment in coal-
producing regions, accompanied by an increase in other regions due to the expansion
of non-fossil fuel power generation, assuming labor mobility is relaxed (Bohlmann et
al. 2019). For the Middle East region, an Integrated Assessment Model (IAM) simula-
tion finds that the large-scale deployment of renewables required to meet the region’s
GHG emissions reduction targets, needs a direct workforce of up to 180,000 and in-
direct workforce of up to 115,000 jobs, in wind, solar Photovoltaic (PV), and Concen-
trated Solar Power (CSP), depending on how much of the technology is manufactured
locally—with the number of direct jobs created per year nearly reaching 18,000 for wind
and well over 90,000 for both PV and CSP (Van der Zwaan et al. 2013).
While these simulation-based studies produce useful insights, their findings are
highly sensitive to the model’s assumptions and specifications. Recent meta-analysis
confirms the sensitivity of estimated net employment effects of the energy transition to
the specific method applied.19 Rigorous econometric estimation, following a specifica-
tion like equation (1), for the energy sector itself remains scarce. Simulation methods
themselves are also open to improvement.20
112 The World Bank Research Observer, vol. 40, no. 1 (2025)
Service
Services exposed to extreme weather conditions will be strongly affected. The trans-
port sector is particularly vulnerable to heat extremes, heavy rains, and flooding. Ex-
tensive adaptation of infrastructure, operations, and service provision will likely be
needed to mitigate impact (Sims et al. 2014). The net employment effect in this sector
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remains understudied globally. Evidence from the United States indicates that trans-
portation payroll expenses declined significantly in hotter than usual years, though it
remains unclear whether this effect stems from reduced labor supply or decreased de-
mand for transportation (Behrer and Park 2017).
Tourism is another service exposed to climate risks. In South Africa, informal work-
ers in tourism-related subsectors in provinces with high tourist activity face the great-
est vulnerability to drought: overall employment in these areas declined by 1.2 percent-
age points, and transport sector employment declines by 0.3 percentage points, for a
1-standard deviation increase in measured drought (Gray et al. 2023).
Similar to the energy sector, the transport industry is vulnerable to both climate-
related conditions and the risks associated with climate transition—think economy-
wide carbon pricing, climate-smart transport policies, new transportation solutions,
among others. As one of the leading contributors to carbon emissions, responsible for
24 percent of CO2 emissions from fuel combustion on a global scale, the transport sec-
tor is expected to undergo a significant transformation (IEA 2020). These long-term
and far reaching effects are often overlooked in analysis, but are crucial for shaping ef-
fective policies. The section on Government policy response discusses policy examples,
including carbon tax and energy pricing, and their impact on employment.
Labor Supply and Time Allocation
Changes in climate may affect labor supply and time use in various ways. First, weather
conditions may encourage worker absenteeism. In India, a rise in the 10-day average
temperature by 1◦ C WBGT21 increased the probability of absenteeism among steel
workers by 2 percent and among garment workers by 10 percent, but not for piece-
rate workers in cloth-weaving factories (Somanathan et al. 2021). Workers in Chinese
manufacturing, however, are not more absent on hotter days (Cai et al. 2018).22
Weather conditions may also encourage shirking. The quality of reported data
among Demographic and Health Survey (DHS) interviewers declined significantly on
hot days, with more missing responses per interview on average, while the total num-
ber of interviews conducted per interviewer remained unchanged (LoPalo 2023).23
Second, workers may adjust the distribution of work time in response to weather
shocks. In one example work time is reallocated from farm to off-farm work. Indian
households that experienced a persistent annual decline in farm income due to drying
up wells (caused by climate change) are 12 percentage points more likely to derive in-
come from off-farm employment. These added earnings compensate for income lost,
Feriga, Lozano Gracia, and Serneels 113
leaving total household revenue unaffected (Blakeslee et al. 2020). Neither work related
migration nor employment in nearby villages, nor changes in agricultural practices,
played a similarly large role in offsetting the fall in income. Identical shifts to non-farm
activity have been observed in rural China.24 Rainfall shocks induce analogous behav-
ioral patterns. In Brazil more work hours are allocated to non-agricultural activities at
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times of negative rainfall shocks, with stronger effects for households in low-income
municipalities (Branco and Feres 2021).
In some cases, workers seem to reallocate time between leisure and labor. Work-
ers in heat-exposed industries in the United States reduce their daily labor supply by
as much as 1 hour at temperatures above 85◦ F (29◦ C); this reduced work time is pri-
marily replaced by indoor leisure (Graff Zivin and Neidell 2014).25 In another US study
male workers labor 30 minutes longer on bad weather or rainy days (up to 48 minutes
in dry regions), and reduce work time on the day following a rainy day to make up for
the previous day’s lost leisure (Connolly 2008). It is unclear whether shifting activities
across time is as common in developing countries, as the existing evidence is mixed. In
rural China, more growing-season Harmful Degree Days—defined as days with temper-
atures above 32◦ C—increases the probability of a time shift to leisure for men (Huang
et al. 2020). The aforementioned DHS interviewers work more hours per day during
hotter than usual periods.26
Labor supply response may also vary by gender. Women tend to have less oppor-
tunity to diversify into other tasks at times of weather shocks, possibly exacerbating
existing gender gaps in the labor market.27
Together these studies suggest that workers may adapt their labor supply in the
short term in response to changes in climate. Most of the current studies abstract from
adaptation, which remains understudied.28
A growing literature studies the possible mechanisms behind the observed short-
term changes in labor supply. Early work, primarily based on macro analysis, conjec-
tured the decline in the temperature-sensitive agricultural sector as a major factor
(Jones and Olken 2010; Dell et al. 2012). Recent evidence underlines the importance
of health (Deschenes 2014).
Exposure to extreme temperature has a direct impact on the body: it strains car-
diovascular, respiratory, and cerebrovascular systems. This also leads to increased
mortality risks. Descriptive empirical findings unveil a U-shaped relationship between
extreme temperatures and heightened mortality, particularly pronounced in poorer
countries where death rates are disproportionately higher, up to 50 percent in some
instances, compared to more affluent countries (Carleton et al. 2022). An additional
hot day with a mean temperature above 36◦ C increases annual mortality in India by
0.75 percent (Burgess et al. 2017). In the United States, the number of days with tem-
perature above 90◦ F (< 32◦ C) is associated with a 0.11 percent increase in mortality,
after accounting for adaptation at home (Deschenes and Greenstone 2011), while an-
nual mortality is estimated to increase by up to 3 percent by 2100.29 Hot-day related
114 The World Bank Research Observer, vol. 40, no. 1 (2025)
fatalities fall with access to residential air conditioning (Barreca et al. 2016). Average
temperature is also positively related to suicide rates and negatively related to mental
well-being, with adaptation (like air conditioning) having little impact. A 1◦ C increase
in the monthly average temperature increases suicide rates in Mexico 2.1 percent, com-
pared to 0.7 percent in the United States (Burke et al. 2018).30
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The health effects of temperature are further aggravated by air pollution (Graff Zivin
and Neidell 2013) and humidity (Barreca 2012), both of which negatively impact health
directly. Not controlling for humidity in equation (1), leads to 0.9 percent lower mor-
tality rates for the United States, underestimating the potential cost of climate change.
Natural disasters also lead to death. Examining the 2004 Indian Ocean Tsunami
in Indonesia as a natural experiment, a study finds that individuals lacking physical
strength, such as children and older adults, and those who could not find help from
a physically strong person, were less likely to survive the calamity (Frankenberg et al.
2011).
A sub-strand of the literature looks at long-term effects on human capital, and finds
that climate change represents a major threat to health and education during child-
hood, which are likely to affect employment outcomes later in life.31
On-the-Job Productivity, Wages
Several studies examine how climate change affects on-the-job productivity. These pa-
pers tend to follow the approach set out in equation (1), but the level of observation
differs, leading to estimation at the micro, meso, or macro level, which each measure
and define worker productivity in their own way. Micro studies focus on observed in-
dividual worker performance, output or wages as the left-hand side variable.32 Studies
at the sector or economy wide level typically consider output per worker, using an ag-
gregate economic indicator such as output, value-added, or GDP, divided by reported
number of workers employed.33
Increased temperature generally has an adverse effect on labor productivity, and this
seem to hold across settings, specifications, levels of observation, and types of output.
This is confirmed by a recent review of a subset of studies (Lai et al. 2023). Micro-level
analysis in developing countries finds a negative relationship between productivity and
temperature across various industries and occupations. In India’s manufacturing sec-
tor, a 1◦ C increase on a hot day reduces labor productivity by 2–4 percent (Somanathan
et al. 2021). For garment factories in India, productivity effects of relatively hot days
are in the magnitude of -2 pp/+1◦ C for temperatures above 27◦ C-28◦ C (Adhvaryu et
al. 2020). Workers’ data from non-climate-controlled manufacturing firms in China
suggest a U-shaped relationship: temperature is positively correlated with productiv-
ity up to 78◦ F (∼25◦ C), after which productivity declines with further warming (Cai et
al. 2018). High-wage professions, such as professional tennis players, exhibit a nega-
tive response to hot temperature, with player performance observed to decline in both
Feriga, Lozano Gracia, and Serneels 115
concurrent, and to some extent, subsequent games, particularly noticeable among
less-experienced players (Burke et al. 2023; Picchio and Van Ours 2023).
In many settings, the output of individual workers is not readily observable. Still,
much can be learned from analyzing labor productivity at the firm level. One approach
is to consider the change in firm Total Factor Productivity (TFP) obtained from esti-
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mating a production function. Firm-level analysis of 500,000 Chinese manufacturing
plants finds heat stress to be strongly related to economic output (Zhang et al. 2018):
a day with a temperature above 90◦ F (32◦ C) lowers TFP of the average manufacturing
firm by 0.56 percent relative to a day with a temperature between 50–60◦F (10–15◦ C),
resulting in 0.45 percent lower output. Productivity losses tend to be larger for work-
ers in heat-exposed industries.34 In Chinese manufacturing, the largest losses in value
added per worker on hotter summer days occur in industries where workers are highly
exposed to heat while air conditioning is largely infeasible; these include ferrous metal
mining (-48.7 percent/+1◦ C) and timber (-41.2 percent/+1◦ C) (Chen and Yang 2019).
Losses in agricultural TFP in China are estimated at 2.6 percent for every additional
day with a temperature of more than 33◦ C during the year (Chen and Gong 2021).
These findings are consistent with county-level payroll evidence from the United
States indicating that labor productivity in highly-exposed non-agricultural industries,
such as utilities, mining, and manufacturing, declines by as much as 50 percent on a
workday above 95◦ F (35◦ C), a nine-fold impact relative to less exposed industries, al-
though there seems to be adaptation when evaluating cross-county differences (Behrer
and Park 2017).35
In indoor controlled environments, productivity tends to be similarly negatively as-
sociated with temperature, often in a non-linear, U-shaped way. A meta-analysis of in-
door office, laboratory, and classroom settings finds task performance to increase until
reaching a ceiling at 21- 22◦ C, with further increasing temperature from 23◦ C to 30◦ C
resulting in a 9 percent productivity loss (Seppanen et al. 2006). Observational find-
ings from call centers in India appear to indicate a decline in worker productivity of as
much as 1.8 percent for each 1◦ C rise within a temperature range of 21. 9◦ C to 28. 5◦ C
(Niemela et al. 2002). Student performance among college entrants in China declined
by 2.9 percent of a standard deviation for a 1◦ C increase in mean temperature of 23◦ C
during the exam period—with a more pronounced effect for high-performing students
(Graff Zivin et al. 2020). These impacts are larger than those found among students of
similar age in New York (-1.60 percent/+1◦ C) (Park 2022), possibly due to lower unob-
served adaptation, like access to air conditioning.
Health is increasingly seen as an important mediating factor. When heat exceeds
certain thresholds, negative physiological responses such as respiratory disease and
cardiovascular strain, higher levels of fatigue and exhaustion, heat strokes, and cog-
nitive impairment can affect body and brain functioning, typically reducing work ca-
pacity while on-the-job and potentially leading to work-related injuries (Zander et al.
2015; Park et al. 2021; Filomena and Picchio 2023). One adaptive response to cope with
116 The World Bank Research Observer, vol. 40, no. 1 (2025)
these effects is to reduce labor effort. DHS interviewers reduce their labor effort on
hotter and more humid than usual days, completing 13.6 percent fewer interviews per
hour (LoPalo 2023). Descriptive analysis for India finds that rice harvesters self-report
heat exhaustion, pain, signs of cardiovascular strain, during work on hot days, which
caused a reduction in the number of rice bundles collected per worker—the equivalent
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of 5 percent lower hourly productivity per worker per 1◦ C at WBGT < 26◦ C (Sahu et al.
2013). Emerging evidence suggests that workers may acclimatize to heat exposure as
the body may develop heat tolerance over time, which may mute productivity loss, but
more research is needed to understand this relationship.36
An alternative response is to increase the number of rest breaks. Field experimental
evidence from Indonesia finds workers in deforested settings that lack natural cool-
ing services to be 8 percent less productive and take 44 percent more breaks; they are
found to have a 0.14◦ C higher median core body temperature and experience 39 per-
cent chance of moderate hyperthermia (Masuda et al. 2021). Taking more breaks was
found to be the key mechanism through which productivity declined, as work effort
measured by physical activity using an accelerometer showed no differences between
the two groups.
These micro impact estimates provide important complementary insights to
macro-level estimates. First, they suggest that the latter may underestimate the eco-
nomic costs of climate change when not taking on the job productivity losses into
account. Second, they present a channel through which output losses occur, formally
testing a hypothesis made by early research.37
The primary focus on short-term effects, and limited attention for adaptation, may
mean that these impacts will be smaller in the long run. Nevertheless, magnitudes of
productivity losses may be substantial.38
Income, Vulnerability Among Self-Employed
Changing weather patterns increase vulnerability and variation in income of the self-
employed, especially in agriculture, increasing the risk of poverty. Recent analyses us-
ing a daily poverty threshold of $1.90 reveal that with each 1◦ C increase in tempera-
ture, the headcount poverty rate escalates by up to 2.1 percent. The impact is most pro-
nounced in agriculture-dependent regions in Sub-Saharan Africa and South Asia (Dang
and Trinh 2022). Particularly vulnerable to climatic variability are smallholder farmers.
In comparison to larger farms, smallholders in a sample of Sub-Sahara African coun-
tries experience, on average, a 20 percentage point decrease in per capita expenditure
and a 15 percentage point rise in the extreme poverty rate—largely attributable to flood
shocks, and to some extent, drought shocks (Azzarri and Signorelli 2020). The poorest
populations, which typically include smallholder subsistence farmers, are arguably hit
hardest and most frequently by climate change events.39 Their livelihoods depend more
on natural assets like land and livestock, tend to be concentrated in climate-sensitive
Feriga, Lozano Gracia, and Serneels 117
activities, such as farming, forestry and capture fisheries, and they are more likely to
live in hotter, drier, and more flood-prone locations (Nordhaus 2006).
The rural poor in developing countries typically have limited capacity to cope with
and manage the risks arising from negative income shocks, as shown by a large body
of research, including shocks stemming from climate variability. For instance, house-
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holds’ main liquid assets hardly recovered to pre-famine levels a decade after the
famine of the mid-1980s in the Horn of Africa, even though they had used multiple cop-
ing strategies. Cattle holdings were, on average, two-thirds in value terms of what they
were before the famine (Dercon 2002). Additionally, we have limited understanding of
long-term adaptation in agriculture in developing countries. Existing work suggests
that adaptation is often sub-optimal, both across space and time, as we discuss in the
section on Firm, farm, household and worker response, possibly due to poor incentives
or high cost of adaptation, and more research is needed.40
Rigorous research on the distribution of impacts—for instance across income levels
or occupations—remains mostly absent for developing countries.41 The most advanced
insights in this area are those related to reallocation and migration, which is what we
turn to next.
Reallocation of Labor
The impacts of climate change on labor demand, supply and productivity may in turn
lead to a reallocation of labor. Climate migration has received much attention in the
literature. 42 Change in the sector of work—which may coincide with migration—has
been studied less. We discuss each in turn.
Sectoral Reallocation
Recent research, primarily conducted in India, demonstrates how agricultural produc-
tivity shocks caused by changing weather conditions can lead to a reallocation of labor
from agriculture to manufacturing and services. Between 2001 and 2007, a rise of 1◦ C
in the daily average temperature corresponded to a decrease of 7.1 percentage points
in agricultural employment, an increase of 2.0 and 3.4 percentage points in manufac-
turing and services employment, respectively, and a 0.7 percentage point increase in
district-level unemployment (Colmer 2021). Rural households that have experienced
depletion of their first borewell within the past ten years are four percentage points
more likely to see their adult members engaging in non-agricultural off-farm work
(Blakeslee et al. 2020).
Flexible and integrated labor markets can facilitate reallocation and expansion in
the receiving sector. In more flexible local labor markets in India, an increase in the
daily average temperature is linked to a 10 percent growth in output and a 14.6 percent
increase in the number of contract workers in formal manufacturing firms. Conversely,
118 The World Bank Research Observer, vol. 40, no. 1 (2025)
in the less flexible labor markets, there was a decrease in employment, marked by a
12.9 percent reduction in firm output and a 14.8 percent decrease in the proportion of
contract workers (Colmer 2021). The majority of workers who are reassigned in the less
flexible labor markets transition to smaller, informal manufacturing companies.
Other research confirms that climate change may accelerate structural change. In
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Brazil, economic activity in localities with higher incidence of droughts during 2000–
2010 shifted more rapidly towards manufacturing, where employment increased nearly
8 percent while agricultural employment reduced by 11 percent (Albert et al. 2021).
Among Indian rural households whose first borewell failed, the capacity to diver-
sify into non-agricultural employment hinged on the structure of the local economy
(Blakeslee et al. 2020). Individuals were more likely to leave their agricultural jobs in
villages with more and larger firms and a more industrialized rural economy.
Local demand also matters. Falling agricultural employment and productivity can
lead to lower demand for labor in the non-tradeable sector through backward and for-
ward linkages, while manufacturing employment may at the same time expand with
labor becoming cheaper (Albert et al. 2021; Liu et al. 2023). Likewise, an increase in lo-
cal demand can promote the shift of labor from agriculture to non-agricultural sectors
in response to positive rainfall shocks, as higher incomes stimulate greater demand for
local products and services. Estimates show that a 1-standard deviation above local av-
erage rainfall during the main growing season in rural India increased the probability
of households having a primary occupation in the non-agricultural sector by 1.1 per-
centage point (Emerick 2018).
The influence of climate change on structural transformation is garnering growing
interest and presents a promising avenue for future research.43
Climate Migration
Variations in climate can affect people’s choices to migrate in search of employment
opportunities.44 Several aspects of this relationship warrant investigation.
Multiple studies observe a strong correlation between migration and rising temper-
atures. Higher temperature induces within-country migration from rural to urban ar-
eas in Mexico, as well as cross-border migration to the United States (Nawrotzki et al.
2015; Jessoe et al. 2018). An additional Harmful Degree Day—defined as a day with an
increase in temperature from 32. 5◦ C to 33. 5◦ C—increases the probability of migrating
to urban areas and to the United States by 1.4 percent and 0.3 percent, respectively.
In El Salvador, an extreme rise in temperature decreases agricultural productivity and
total production. To compensate the loss in income and escape poverty, farmers cut
back their demand for hired workers, which in turn leads to out-migration, includ-
ing to the United States (Ibanez et al. 2022). Heat stress is found to be strongly re-
lated to long-term migration in Pakistan, particularly for men who mostly move long
distances, including abroad, while women often move within geographical areas and
Feriga, Lozano Gracia, and Serneels 119
villages (Mueller et al. 2014).45 In Indonesia, higher temperature raises the probability
of permanent household migration in non-linear ways, by 0.8 percent when the mean
temperature increases from 26◦ C to 27◦ C, and by 1.4 percent when it increases from
27◦ C to 28◦ C (Bohra-Mishra et al. 2014).
Migration is also related to decreases in rainfall. Evidence for Indonesia indicates
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that agricultural households affected by unanticipated low rainfall are more likely to
have a household member migrate to a nearby area (Kleemans and Magruder 2018). In
China, a one-standard-deviation decline in rainfall, signifying a negative rainfall shock
compared to the long-term average, is associated with a 4.5 percent reduction in agri-
cultural labor and a 5 percent increase in migration to urban areas. This effect is partic-
ularly pronounced among younger individuals (Minale 2018). In Bangladesh, the like-
lihood of internal migration rises when a higher percentage of households face crop
failure due to flooding (Gray and Mueller 2012).
Studies examining fluctuations in both temperature and precipitation indicate that
the relationship between precipitation and migration is less robust and less pro-
nounced compared to that with heat and rising temperatures. While rainfall and flood-
ing can prompt temporary and short-distance movement in Pakistan and Indone-
sia, permanent migration tends to be predominantly linked to temperature increase
(Bohra-Mishra et al. 2014; Mueller et al. 2014; Kleemans 2015). Recent studies for the
United States illustrate that these migration patterns can play a pivotal role in shaping
the geographic landscape of population growth (Obolensky et al. 2024).
Research on the migration impact of natural disasters tends to find a strong re-
lationship, though evidence for developing countries remains scarce. In the United
States, regions affected by tornadoes in the early 1900s exhibit increased net emigra-
tion, whereas areas subject to flooding experience greater inward migration, likely
due to the government’s reconstruction efforts in flood-prone regions, rendering them
more appealing for economic activity (Boustan et al. 2012).
In a similar vein, the Dust Bowl of the 1930s in the United States, characterized by
severe dust storms and drought that ravaged the US Great Plains, was found to trigger
substantial emigration from impacted regions, causing a decline in the local popula-
tion (Hornbeck 2012).46 Migration prompted by natural disasters may mediate lower
local economic growth. On average, annual economic growth fell by 0.45 percentage
points in hurricane-exposed US coastal counties, of which 28 percent are attributed
to the relocation of wealthy individuals (Strobl 2011). Scarce evidence for developing
countries indicates that international migration to OECD countries is positively re-
lated with natural disasters in the migrants’ home country. However, the intensity of
this relationship fluctuates depending on the nature of the disaster and the geograph-
ical region in question (Drabo and Mbaye 2015).
Climate migration seems to accelerate urbanization up to some limit. Precipita-
tion is positively related to urbanization in Sub-Saharan Africa (Barrios et al. 2006).47
120 The World Bank Research Observer, vol. 40, no. 1 (2025)
But moisture, measured as a function of precipitation and evapotranspiration, is neg-
atively associated with the absorption of farm workers into the urban labor force
across 29 African countries, especially in cities with a strong manufacturing presence
(Henderson et al. 2014).
Causal identification remains a challenge when analyzing climate migration. Im-
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pact estimates may reflect pull as well as push factors, and attribution to a single el-
ement is often impossible (Mueller et al. 2020).48 The implication for policy is that, in
many instances, it may be more effective to promote overall labor market integration
rather than focusing on specific groups or measures. However, there is a unique case
when it comes to conflict, which is frequently overlooked in the analysis.49
While the above studies look at actual changes in climate, recent research for the
United States finds that individuals’ expectations of deteriorating future climate can
significantly lead to relocation (Bilal and Rossi-Hansberg 2023).
In general, climate migration tends to be seen as a coping strategy to alleviate liq-
uidity constraints stemming from climate-related shocks (Kleemans 2015; Bazzi 2017),
but at the same time frequently necessitates access to capital (Cattaneo and Peri 2016).
Reduced mobility barriers can amplify the role of migration as an adaptive measure
where relevant, effectively mitigating a substantial portion of the welfare losses high-
lighted in climate studies (Cruz and Rossi-Hansberg 2021; Conte 2022).
Response to the Impact of Climate Change on Labor
Economic actors respond to the labor impact of climate change both through adap-
tation and mitigation. This section examines the observed responses of firms, farms,
households, and workers to the first order labor impacts of climate change discussed
earlier, with subsequent focus on government policy responses.50
Methods and Identification
How do economic agents respond to a manifested impact of climate change on labor?
The nascent literature in this area follows one of three approaches when analyzing the
response of firms and workers.
A first group of studies investigates the extent of adaptation, regressing observed
adaptive behavior on temperature. A common example is a firm’s adoption of air con-
ditioning to improve worker productivity. The analysis is similar to the one presented
in equation (1), with the adaptation behavior as left-hand side variable.51
A second family of papers focuses on how adoption moderates the impact of climate
on labor. In this approach adaptation enters as a right-hand side variable in equation
(1) where it is interacted with the climate variable. For instance, one can study whether
the adoption of AC alters the impact of temperature on worker productivity. While
this does not amount to causal testing of the underlying mechanism, the obtained
Feriga, Lozano Gracia, and Serneels 121
estimates enable assessment of whether the impact of climate—in this case
temperature—on worker productivity varies with adoption.52 The same approach can
be used to evaluate the adaptation capacity of specific subgroups (e.g., gender and in-
come in the case of workers, size and profitability for firms; firms; regions; or indus-
tries).53
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A third approach compares the short-term impact estimates obtained from equa-
tion (1) with estimates obtained from a long-term-difference equation (which differ-
ences out short-term unobserved changes) to get an approximation for adaptation
over time (see Dell et al. 2014; Hsiang 2016). This approach can be used to evaluate
if short-term impacts are offset in the long term.54
These approaches suffer from some of the limitations discussed earlier, in particu-
lar that actual temperature (or another climate change variable) to which workers are
exposed remain unobserved, and that the estimates are for partial equilibrium only.
How governments respond is typically assessed using the classic tools from impact
evaluation. Few studies are able to report rigorous estimates based on exogenous vari-
ation (e.g., a discrete cut off, or a roll out over time).55 Other thorough work in this area
uses panel data fixed effects estimation of the form presented in equation (1). While
the policy target of interest serves as left hand-side variable, only a limited number of
studies evaluate the impact of policies’ interventions on labor outcomes. Most stud-
ies in this field concentrate on the impact of policies on output or emissions as the
left-hand side variable.
Firm, Farm, Household, and Worker Response
Firms may mitigate the negative impact of higher temperature by cooling the work-
place. Firm adaptation effort depends on the relative cost of adopting climate controls
such as air-conditioning, passive cooling systems, or specific heat saving technology,
versus the gains in output stemming from the resulting improvements in labor supply
and productivity.
Some adaptation measures may therefore be less attractive for low-productivity
jobs. In India, the probability of investing in climate control is substantially lower in
cloth-weaving plants compared to diamond plants (Somanathan et al. 2021). Air con-
ditioning for the average cloth-weaving firm costs nearly 23 percent of the total wage
bill, while estimated productivity loss is about 2–4 percent per 1◦ C rise, making the
investment unattractive for low-markup firms. In contrast, high value-added diamond
plants do provide air conditioning, especially to polishing units, which carry out work
central to diamond quality and value.56
For other technologies, benefits may exceed costs more rapidly. Energy-efficient
LED lighting reduces heat, thereby increasing worker productivity, and at the same
time lowering energy costs (by almost a third), lowering the break-even period from
3.5 years to 8 months (Adhvaryu et al. 2020).57
122 The World Bank Research Observer, vol. 40, no. 1 (2025)
Diverging from a technology adoption response, firms may counter the heat-
induced decline in labor productivity by strategically relocating their workforce to less
heat-exposed areas. Recent findings from the United States indicate that, in response
to heat shocks, the majority of firms reduce their workforce in areas prone to shocks,
whereas large firms with multiple locations augment their workforce in unaffected es-
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tablishments. This stands in contrast to single-location firms which typically experi-
ence downsizing (Acharya et al. 2023). The reallocation of the workforce often mirrors
the shift of production to less heat-exposed regions, a strategy more readily embraced
by large firms. Their considerable size provides them with a natural resilience, enabling
them to better absorb the impacts of weather shocks even as these shocks escalate and
become more frequent (Ponticelli et al. 2023).
Our understanding of how farmers respond to climate change impacts is growing.
Rice farmers in China use a range of adaptation strategies including upscaling irriga-
tion, reseeding, fixing and cleaning seedlings, changing crop varieties, as well as in-
creasing the use of fertilizers and pesticides. While these measures tend to improve
mean yield, only a third of farms employ these at times of flood and drought (Huang
et al. 2015). In Ethiopia farmers report using increased irrigation, soil conservation
to preserve moisture content, drought-tolerant crop varieties, adjusting of planting
time, and planting trees (Deressa et al. 2009). Famers in Bangladesh adopted heat-
tolerant rice crops, switching in the process from rain-fed to irrigation-based vari-
eties (Moniruzzaman 2015).58 Experimental findings from India indicate that the adop-
tion of flood-tolerant rice varieties has improved agricultural productivity of farmers
(Emerick et al. 2016). A similar pattern is observed in South America, where a drier and
hotter climate has encouraged farmers to adopt more suitable crop varieties including
squash, fruits, and vegetables (Seo and Mendelsohn 2008).
While some of these adaptation strategies overlap, others vary by country, setting,
and income group. Whether they reflect an optimal response to local conditions re-
mains an active field of research. In China farmers managed to mitigate 9 percent of po-
tential yield losses by adapting planting dates and growing-season length in response
to contemporaneous changes in temperature. No such or other response is observed
in the long term, suggesting suboptimal behavior (Cui and Xie 2022). In India, hotter
districts were found to adapt for temperature increases in the range of 18◦ C to 27◦ C,
by adopting new agricultural practices and switching to heat-tolerant crops, resulting
in lower yield losses than colder districts that faced the same increase.59 But for higher
temperature rises (>30◦ C), their losses were the same as in cold districts, suggesting
non-optimal adoption (Taraz 2018).
This is broadly consistent with findings in high-income countries. In the United
States, temperature affects maize yields in hot (Southern) and cool (Northern) states
almost identically and there is little variation in the temperature-crop relationship
across time, despite technological advances in farm-level adaptation for warmer cli-
mate (Schlenker and Roberts 2009). In France, equal short- and long-term losses in
Feriga, Lozano Gracia, and Serneels 123
wheat yields (10 percent and 7 percent, respectively), related to a 2◦ C warming also
indicated limited adaptation over time (Merel and Gammans 2021).
Restricted information, incomplete insurance and limited access to credit and ex-
tension services, as well as low adoption of technology itself have been named as bar-
riers to optimal adaptation (Karlan et al. 2014; Huang et al. 2015; Suri and Udry 2022;
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Lane 2023).
Beyond the farm-level response, rural households and workers are found to use multi-
ple strategies to reduce exposure and mitigate impact of climate shocks. These include
common individual and collective strategies to reduce the impact of income shocks,
like diversification, precautionary savings, liquidation of assets, and risk-sharing.60
Among Eastern and Southern African households, about a third adopted alternative
forms of employment and one in six reduced consumption to adapt to climate shocks,
according to descriptive evidence. In Ethiopia and Tanzania over 50 percent of house-
holds used savings or borrowing as a primary response to climate shocks. In Kenya,
two thirds sold livestock as part of changing farming practices (Rahut et al. 2021).61 A
large literature on income shocks shows that, despite the variety of risk-coping strate-
gies they adopt, rural households do not manage to completely insure against income
loss.62 How households and workers can insure themselves against climate-related
shocks is receiving increased attention, and we discuss some new directions as part
of social protection policies below.
Government Policy Response
A comprehensive overview of policies designed and implemented in response to cli-
mate change is beyond the scope of this paper. Our main interest is to what extent
these policies focus on, or take into account, labor outcomes. Our examination of the
relevant literature reveals two issues. First, evaluations of general climate change inter-
ventions seldom include an assessment of their labor impact. Second, climate change
policies that target labor remain understudied. We discuss each in turn.
General Climate Change Policies and Their Impact on Labor
A wide range of policies aim to address challenges related to climate change. It is a very
broad set: from mitigation policies like fossil fuel subsidy reform, over carbon pric-
ing, alternative power generation, change in land use, and altered transport policies,
to adaptation policies like the use of increased crop variety, disaster preparedness and
adaptive social protection. Table A1.1 in Appendix A1 provides examples of policies
that have been evaluated. These appraisals pay virtually no attention to the impact on
labor. On the rare occasion when they do, they tend to focus on employment, showing
sizeable—typically short-term—impacts, and neglect potential effects on other labor
outcomes.
124 The World Bank Research Observer, vol. 40, no. 1 (2025)
As an illustration, consider the example of carbon pricing, which is seen as an at-
tractive policy lever to bring down carbon emissions, reduce global warming, and ac-
celerate the transition to a low carbon economy.63 A simulation for US manufacturing
industries finds that a 1 percent increase in the energy price associated with carbon
tax (resulting in $15/ton carbon) reduces output in the short run by as much as 5 per-
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cent, particularly in energy-intensive sectors, like iron, steel and aluminum (Aldy and
Pizer 2015). Although the study extensively covers the impact on output, it neglects the
impact on labor. Research in this strand typically concentrates on the policy’s impact
on economic output and emissions. But there is much to be gained from examining
the labor-related consequences. One study, exploiting exogenous variation in eligibil-
ity for carbon tax discount across a panel of UK manufacturing firms, finds no causal
evidence of policy impact on plant-level employment, and this result holds across dif-
ferences in firm size, energy intensity, or trade intensity (Martin et al. 2014). In contrast,
an economy wide simulation study for the United States between 1976 and 2007 finds
a sizeable labor impact of higher energy prices in the short term, including a negative
employment effect between -0.10 percent and -0.16 percent (Deschenes 2012).64 These
short-term impacts differ among sectors, with agriculture and transportation experi-
encing the largest employment losses, with estimated cross-elasticities of -0.43 percent
and -0.29 percent, respectively.65 A study for British Columbia finds that annual em-
ployment rose by 0.74 percent over the subsequent six years, compared to the rest of
Canada, when unilaterally introducing a state level revenue-neutral carbon tax in 2008
(Yamazaki 2017). This masks large heterogeneity across industries with employment
falling in emission-intensive and trade-exposed industries but rising in services.66 Im-
pact evaluation of a $40/ton CO2 carbon tax for a 30 percent emission coverage in Eu-
rope (EU + countries) finds positive, but generally not statistically significant, effects
on employment, either immediately or in the subsequent five years of policy implemen-
tation (Metcalf and Stock 2020).
Studies evaluating employment effects for developing countries remain scarce. A
CGE model for the transport sector in Malaysia suggests that carbon tax leads to a fall
in employment across all subsectors—echoing the negative effect on sectoral output,
demand, and investment—as factors of production shift to less energy-intensive, more
labor-intensive sectors in the economy (Solaymani et al. 2015). A recent IMF simula-
tion for manufacturing in Asia and the Pacific utilizes national I/O tables to estimate
the potential short-term impact of a $25/ton carbon tax (Dabla-Norris et al. 2021).
Sectors that rely on carbon-intensive inputs, typically the downstream, extractive and
energy-producing industries, are most affected in terms of output and employment.
The amount of projected job loss is primarily driven by the industry’s energy depen-
dence and share of employment. This varies substantially across countries. In Malaysia
the largest expected job losses are concentrated in electrical and electronics manufac-
turing while in Mongolia the vast majority projected job losses are in wood and paper
manufacturing.
Feriga, Lozano Gracia, and Serneels 125
Table 1. Labor Targeting Climate Change Policies
Impact of climate change on labor Labor oriented policy response
Demand for labor Green jobs
Supply of labor, time use Labor-oriented adaptation, regulation for flexible work
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Productivity Labor-oriented adaptation, regulation for flexible work
Self-employed income, Green jobs, social protection
vulnerability, poverty
Reallocation of labor Labor market integration, social protection
Skills and human capital Green skills, social protection, education and health policies
Source: Author’s own elaboration based on a review of the literature.
Table 2. Examples of Expected Impact of Decarbonization Policies on the Demand for Labor
Policy Expected impact Country or region Method Reference
State-level green policy 1% more green jobs US Historical data Yi (2013)
Regional-level energy shift0.24% increase in EU Simulation Markandya et al.
employment (2016)
Energy efficiency measures 4 million + green US Simulation Wei et al. (2010)
and renewable energy job-years by 2030
targets
Investment in renewables Around five more FTE US Simulation Garrett-Peltier
and energy efficiency jobs compared to similar (2017)
investment in fossil fuels
State-level green policy refers to market-based tools that provide support for renewable energy and energy efficiency.
Energy shift refers to a change from carbon intensive sources toward gas and renewables.
Labor Targeting Climate Change Policies
Our review of the literature points to six potential policies to target the impact of cli-
mate change on labor. We refer to these as: green jobs, green skills, labor-oriented adap-
tation, flexible labor regulation, labor market integration, and social protection. As
shown in Table 1, each of these relate to one or more of the above discussed poten-
tial labor impacts of climate change. In depth or formal evaluation of these policies
has so far remained limited. We discuss each in turn.
Policies that promote green jobs aim to increase net labor demand while transition-
ing to a low carbon economy.67 Although comparing the employment impact of green
employment policies remains relatively rare, a small literature is emerging. Table 2 pro-
vides an overview of key studies in this area. Comparison of impact across studies is
complicated by the use of different measures and methods. The emerging work on
energy transition, predominantly focusing on high-income countries, provides an il-
lustration. One study finds that an additional state-level green policy, like renewable
126 The World Bank Research Observer, vol. 40, no. 1 (2025)
portfolio standards and energy efficiency resource standards, is associated with 1 per-
cent additional green jobs on average across 361 metropolitan areas in the United
States (Yi 2013). These results corroborate the findings from simulation-based stud-
ies that dominate this literature. A multi-regional I/O analysis of the transformation
of the EU energy sector over the period 1995–2009 estimates a net employment effect
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of + 0.2.4 percent, an equivalent of 530,000 jobs by 2009, with one-third attributable
to spillovers between EU countries, i.e., employment generated in one country due to
changes in another (Markandya et al. 2016). Another EU study finds that substantial
job creation can be realized in more flexible labor markets (Blazejczak et al. 2014). A
simulation study for the US power sector suggests that implementing more aggressive
energy efficiency measures and renewable energy standards can create over 4 million
full-time-equivalent (FTE) green job-years by 2030, while clean nuclear energy and a
rise in carbon capture and storage (CCS) to 25 percent and 10 percent of total energy
generation, respectively, can create an additional 500,000 job-years (Wei et al. 2010).
Since green energy tends to be more labor-intensive than high-carbon, these sectors
generate more jobs for a given dollar invested. An I/O analysis for the United States
suggests that $1 million invested in fossil fuels is expected to generate 2.65 FTE jobs
versus 7.49–7.72 FTE jobs when invested in green energy, such as renewables or energy
efficiency (Garrett-Peltier 2017).
The new jobs that come with the transition to low carbon may require new skills, of-
ten referred to as green skills. There is, by our knowledge, very little rigorous evaluation
in this area using historical data. One US study finds that changes in environmental
regulation are positively related to demand for green skills, in particular for technical
and engineering tasks (Vona et al. 2018).68
Cooling technology in the workplace is an important example of labor-oriented
adaptation that may improve worker productivity and labor supply, as well as worker
well-being. As mentioned previously, firm adaptation may be less likely for low-
productivity jobs (Somanathan et al. 2021). Government subsidies can be beneficial
in situations where a change in behavior is considered desirable but faces obstacles,
for instance when the cost of early adoption is high but anticipated to fall substantially
over time, potentially due to increased demand (Stern 2022).
Policymakers have been motivated to introduce regulation regarding occupational
health and heat management to protect workers from heat-related stress. Preventa-
tive measures, such as frequent rest breaks, and hydration and high-sodium intake,
can protect workers from heat-related illness and work accidents. These regulations
are unlikely to be followed in informal firms and settings with weak enforcement. In
China’s manufacturing industries protective measures knew limited implementation
by private firms during periods of elevated temperature (Zhang et al. 2018).
Labor regulation for flexible working may provide an alternative policy. Optimized
working hours allow firms and workers to flexibly adjust work schedules to hot temper-
atures (Connolly 2018). In one example workers can choose to operate more on cooler
Feriga, Lozano Gracia, and Serneels 127
days or cooler hours within a day. US workers possessing greater bargaining power, in-
dicated by stronger outside options, and flexible work arrangements, are more likely to
adjust their work schedules in response to adverse temperature conditions. During pe-
riods of elevated daytime temperatures, they typically shift working hours to the night,
keeping overall labor supply unchanged (Cosaert et al. 2023).
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In another approach, workers decide their days off, relaxing the “consecutive vaca-
tion days” requirements in some sectors; this can also reduce absenteeism. Increased
flexibility is particularly attractive for those with dual job holdings, for instance work-
ing in agriculture while having a second job in trade, as they tend to adjust working
hours toward less exposed activities during extreme temperatures (Huang et al. 2020;
Li and Pan 2020; Branco and Feres 2021).
Labor market integration increases labor mobility across the economy, which in turn
facilitates the reallocation of labor. In India, reallocation in response to increased tem-
perature takes place primarily within districts (Blakeslee et al. 2020; Colmer 2021).69
In other settings, integration with wider labor markets is found to stimulate climate-
induced migration within and across regions, within the country, and internationally
(see, for example, Bohra-Mishra et al. (2014), Mueller et al. (2014), Jessoe et al. (2018),
Kleemans and Magruder (2018), Ibanez et al. (2022)).70
Social protection can help reduce vulnerability and smoothen transition. One active
area of research is shock-responsive social protection. Experimental evidence from
Nicaragua suggests that conditional cash transfers combined with vocational training
or a productive investment grant can help households exposed to weather variability
smoothen their consumption and diversify their economic activities (Macours et al.
2022). Other research focuses on anticipatory crisis financing. Findings from a recent
large-scale randomized evaluation of immediate cash transfers during the 2020 mon-
soon floods in Bangladesh show that timely and quick release of cash support leads
to more effective evacuation, better child food consumption outcomes, and less costly
borrowing in the aftermath of the floods (Pople et al. 2021).
Ways Forward
This paper aims to stimulate research on the impact of climate change on labor. A bet-
ter understanding of this impact is needed for at least three reasons. Much like the
increased attention for other primary factors of production, such as land and finance,
a better grasp of the role of labor will inform policies that aim to promote green growth
and facilitate the transition to zero carbon.71 Secondly, labor serves as a key channel
through which climate change impacts individual’s livelihoods, affecting both their in-
come from employment and their opportunities for work. Recognizing these effects
can guide poverty reduction policies and help avoid strategies that worsen poverty.
Lastly, achieving a shift towards a low-carbon economy requires political, and thus
128 The World Bank Research Observer, vol. 40, no. 1 (2025)
citizen support. Ignoring the influence of climate change on labor may undermine such
backing; acknowledging it has the potential to strengthen it.
While the evidence in this field is expanding, its availability remains limited, par-
ticularly for developing countries. Four observations stand out from reviewing the ev-
idence, and in turn suggest ways forward for future research.
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To begin with, there exists at least some evidence pertaining each of the different la-
bor themes identified. Table A2.1 in Appendix A2 provides a visual summary. In some
instances, the evidence originates from research in high income countries, notably the
United States. Even so, this sets an initial benchmark for subsequent studies, offering
valuable insights into both research methods and the expected scale of the relation-
ships under investigation. Recent efforts increasingly study developing countries. Sec-
ondly, temperature changes receive the greatest attention, followed by changes in pre-
cipitation. Climate change events like rising sea levels, tropical cyclones, and droughts,
receive less attention, in part due to their relatively rare occurrence, and limited high-
quality data. Thirdly, certain regions, particularly low-income countries, sub-Saharan
Africa, and the Middle East, lack sufficient evidence.72 Fourthly, despite our emphasis
on the highest-quality evidence, limitations persist in existing analyses. Most studies
omit adaptation, focusing on short-term impact estimates, partial equilibrium analy-
sis, and effects from past events. Ongoing efforts to address these shortcomings involve
economy-wide analysis and more precise forecasting of future climate change events,
both of which pose challenges. New research explicitly models adaptation by studying
its adoption, how it moderates the impacts of climate change, or by comparing short-
term impacts with those of the long term.
With these consideration, three promising avenues for future research emerge.
Firstly, there is a need to deepen understanding in areas where initial knowledge ex-
ists, incorporating adaptation and improving causal analysis of historical data. On the
demand side, conducting sector-specific and economy-wide analyses of labor demand
would enhance our understanding. Of particular interest are sectors where climate
change may increase employment, like the green energy sector. There is also a call
for more research on the potential negative impact of climate change on productivity,
and whether climate change might offer opportunities for development by expedit-
ing structural transformation. Future-oriented analysis can progress through two ap-
proaches. Comparing and integrating historical and simulation analyses would signif-
icantly enhance our knowledge base. A newer strand incorporates social adaptive be-
havior at scale, considering socioeconomic variables and using theoretical work to en-
hance predictions and understanding of societal behavior (Mattauch et al. 2018; Besley
and Persson 2020; van der Ploeg and Venables 2022).
Secondly, in themes with advanced evidence, replication and comparison are
crucial. Key areas include the negative impact on employment in agriculture and
possibly heath exposed manufacturing. Further research is needed for a detailed
understanding of the causal impact of climate change on migration, accounting for
confounding factors.
Feriga, Lozano Gracia, and Serneels 129
Thirdly, themes lacking evidence require a thorough assessment to identify the most
pressing questions. This will, in turn, highlight the need for improved data. For in-
stance, little is known about the employment effects of climate change on the trans-
port sector, which will undergo major change with the transition to green energy. The
impact on the earnings and income variability of the self-employed are not well under-
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stood, and there is a lack of clarity on how impacts vary across the income distribution
more generally. In terms of government policy, incorporating the consequences for la-
bor when evaluating general climate change policies would allow for a systematic com-
parison. Formal evaluation of the identified labor-targeting policies—green jobs, green
skills, labor-oriented adaptation, flexible work regulation, labor market integration,
and social protection—remains scarce overall, particularly for developing countries.
We conclude this review with a more optimistic outlook than when we began. Al-
though there is still much work ahead, clear pathways have emerged. Combined, these
research and policymaking efforts will help determine the optimal paths for achieving
an equitable transition to a low-carbon economy.
Funding
Part of the research time for this work was supported by The World Bank, SMNDR unit.
Conflict of interest
The authors have no conflicts of interest to declare.
Data Availability Statement
No new data were generated or analysed in support of this research.
Notes
Nancy Lozano Gracia (corresponding author) is Lead Economist at The World Bank; her email
address is nlozano@worldbank.org; Moustafa Feriga is Consultant at The World Bank (email:
moustafa.feriga@gmail.com); Pieter Serneels is Professor of Economics at the University of East Anglia,
UK (email: p.serneels@uea.ac.uk)
We would like to thank the following people for their comments and reflections: Hoda Assem, Daniel Le-
derman, and Roberta Gatti. We are grateful to Brenan Gabriel Andre for his support with cartography.
The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors.
They do not necessarily represent the views of the International Bank for Reconstruction and Develop-
ment/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank
or the governments they represent. All remaining errors are ours.
1. Some estimates suggest that a 1◦ C warmer year reduces income per capita by 1.4 percent on average,
with substantial variation across countries, and greater impact in developing countries (Dell et al. 2012).
130 The World Bank Research Observer, vol. 40, no. 1 (2025)
Changes in precipitation have large impact for some countries, especially in sub-Sahara Africa, but not all
(Barrios et al. 2010). Extreme weather events, such as cyclones, also cause substantial economic losses,
especially in countries where they are less frequent (Hsiang and Jina 2014).
2. The poor typically depend more on income from agriculture, which is affected by weather events,
and tend to be more exposed to increased heat and floods (see Hallegatte et al. 2016 for a discussion on
climate change and poverty). They also have less access to effective management and coping mechanisms.
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Vulnerable groups include ethnic minorities, people with disability, and refugees.
3. In the climate change literature, this is sometimes referred to as the High Dimensional Fixed Effects
(HDFE) approach. See Lai et al. (2023), who provide a review of a specific subset of studies on temperature
and worker productivity following a similar approach.
4. Using micro-level worker, firm or household data, or meso- or macro-level sector, region or country
data.
5. The climate change literature increasingly uses temperature bins, also to allow for possible nonlin-
earity in the relationship (see Dell et al. 2014 for a detailed review of the climate—economy literature).
6. An extreme example is when workers operate in AC environment without this being registered.
7. Omitted climatic and atmospheric variables that may have a direct or joint impact on the outcome,
like precipitation, humidity, ambient air pollution in the case of temperature, may bias estimates or render
them less precise.
8. In a companion study we carried out an overview study focusing on the Middle East and North
Africa region, where both COP 27 and COP 28 take place. The draft results are available upon request.
9. Key is the “iterated expectations” assumption that no unanticipated changes take place in expecta-
tions, which is unsatisfactory, lays at the base of the models’ fragility, and makes them “least performing
when they are most needed.” See Hendry and Mizon (2014) for a discussion.
10. An additional 1-day cumulative exposure to temperatures above 33◦C during a single year is found
to have a negative short-run impact of 4.4 percent on agricultural yield across counties in China (Chen
and Gong 2021). Annual growth rate of rice yields declined by up to 30.6 percent across farms in Asia, pri-
marily due to the negative impact of higher minimum temperature on its vegetative and ripening phases
(Welch et al. 2010). Higher temperature reduces output for major crops in Sub-Saharan Africa, with ex-
pected reductions of 22 percent for maize, 17 percent for sorghum, 17 percent for millet, 18 percent for
groundnut, and 8 percent for maize by midcentury. Countries with the highest average yields are expected
to encounter the largest losses (Schlenker and Lobell 2010). Earlier draft work for Indian districts over a
40-year period finds major crop yields to decline by 9.5 percentage points if mean temperature of a single
day shifts from 29◦ C to 31◦ C, which is estimated to lead to 5–9 percent decreases by mid-century (Guiteras
2009).
11. In Indonesia, district rice output increased by 0.4 percent for a 10 percent increase in rainfall
(Levine and Yang 2014). In the United States, an additional week of drought reduced corn and soybean
yields by up to 1.2 percent in dryland counties and 0.5 percent in irrigated counties—though the magni-
tudes are region-specific (Kuwayama et al. 2019).
12. In the Caribbean, one-standard deviation increase in exposure is associated with a 1.8 percent
decline in output in agriculture, hunting, and fishing (Hsiang 2010).
13. The drop in rural employment would be the equivalent of an accumulated 236,094 fewer employed
individuals by 2075 under the Representative Concentration Pathways (RCP) medium emissions scenario
(Jessoe et al. 2018). Representative Concentration Pathways (RCPs) are scenarios used by Intergovern-
mental Panel on Climate Change (IPCC) for climate modelling and research. Each pathway describes a
particular—and possible—climate future depending on how much of greenhouse gases can be emitted in
the coming years. Generally, the RCP 8.5 is considered the worst-case scenario (∼5◦ C increase by 2100 rel-
ative to pre-industrial baseline), while RCPs below that are considered more stringent in terms of climate
mitigation efforts required—the most stringent being RCP 1.9 (∼1.5◦ C, adopted by the Paris Agreement)
and RCP 2.6.
14. Heat exposed industries here include construction, mining, and manufacturing with outdoor work
activity or heat generating indoor production process that do not use climate controls.
Feriga, Lozano Gracia, and Serneels 131
15. India is exposed to almost 10 percent of the world’s cyclones.
16. The literature distinguishes between intensive margin, which is characterized by increased elec-
tricity use (e.g., AC, heaters) and extensive margin, which includes longer-run investments in, for instance,
cooling/heating systems installation, energy-efficient homes, and irrigation (see Auffhammer and Mansur
2014; Carleton and Hsiang 2016 for an overview).
17. Evidence for the United States demonstrates how energy consumption is highest on cold and
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hot days, and attenuates mortality impacts of temperature extremes. US annual residential energy con-
sumption is expected to grow by some 15 percent to 30 percent by the end of century (Deschenes
and Greenstone 2011), and up to 55 percent in some states such as California (Auffhammer and
Aroonruengsawat 2011).
18. Employment outside the energy sector may also fall during the transition to a zero-carbon econ-
omy. For instance, derived labor demand in energy-intensive industrial sectors may fall when increased
energy prices lower primary demand for energy (Deschenes 2012; Kahn and Mansur 2013; Pestel 2019).
19. For instance, CGE models that account for induced employment changes outside the supply chain
of interest are 43 percent more likely to report lower net employment effects, according to a meta-analysis
of net employment effects of renewable energy in the literature (Stavropoulos and Burger 2020).
20. For a discussion on how to improve IAM models, see for instance Dietz and Stern (2015), who
lay out the method’s shortcomings and conclude that improved modelling calls for strong controls. The
models’ shortcomings lie in what are otherwise their strengths: they are good at addressing small pertur-
bations (which is useful when marginal change is the focus of attention). Underlying assumptions that
abstract from the endogeneity of growth, convexity of damage, and the valuation of tail risk, make the
models ill equipped to study structural shifts that may be required in the case of climate change.
21. Wet bulb globe temperature (WBGT) is a commonly used measure for heat exposure on humans,
which takes into account temperature, humidity, wind speed, sun angle, and radiation.
22. For other evidence, including from simulation studies, see Zhao et al. (2021).
23. The study analyses 9,000 DHS interviewers across 46 countries.
24. Years of elevated temperature over the period 1989–2011 reduce the probability of working in agri-
culture as primary employment, with workers with a secondary occupation engaging more in non-farm
work that is less exposed to heat, even though this shift comes with lower earnings on average (Li and Pan
2020). The projected increase in mean temperature in China as a whole is associated with an expected
shift in labor supply from farm to off-farm work of 9–36 percent by 2100 depending on the RCP climate
scenario, corresponding to an increase in off-farm labor supply by about 44.1 million workers, when not
taking possible adaptation into account (Huang et al. 2020).
25. Within-day substitution happens mostly toward the end of the day. Workers do not shift activities
across days. The study uses worker data from time use surveys, linked to weather data over the 2003–2006
period.
26. They reshuffle interview activity to start their work earlier in the morning when temperature is
lower, to keep the number of interviews per day unchanged, in accordance with supervisor’s instructions
(and under a fixed daily wage contract) (LoPalo 2023).
27. Suggestive evidence for China shows that higher temperature is associated with a larger decline in
time allocated to farm work and a smaller increase in time allocated to off-farm work, for women relative
to men (Huang et al. 2020). During hotter temperature, female workers are relatively more likely to reduce
their working hours, have a lower probability of being employed in an agricultural job, and face reduced
wages (Li and Pan 2020).
28. This is also relevant for forward-looking studies. A cross-country simulation predicts a loss of about
2–4 percent of total working hours worldwide by 2030 due to high temperatures, equivalent to 80–136
million full-time jobs—depending on the climate scenario and the amount of agricultural and construction
work assumed to be carried out in the shade (Kjellstrom et al. 2019).
29. This assumes a business-as-usual climate scenario. Findings are similar when controlling for hu-
midity and using residential energy consumption for air conditioning as a measure of adaptation, though
impacts vary across distribution and region (Barreca 2012).
132 The World Bank Research Observer, vol. 40, no. 1 (2025)
30. Impacts are observed across different age groups, including working adults, children and elderly.
Children and elderly are particularly vulnerable. Children at a young age have lower capacity to dissipate
heat and are more vulnerable to climate-induced infections and vector-borne disease, which are among
the primary reasons for child mortality in low- and middle-income economies (see Hanna and Oliva 2016
for a discussion). In the United States, mortality impacts at the hottest and coldest days are more pro-
nounced for people aged 65+ (Deschenes and Greenstone 2011).
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31. Young children are found to be at heightened risk of ill health and low birth weight due to extreme
temperature in the United States and elsewhere (Deschenes et al. 2009; Banerjee and Maharaj 2020) as
well as risk of maltreatment, according to new research (Evans et al. 2023). In Ecuador, a 1◦ C higher-than-
average in-utero temperature exposure is found to be related to 0.7 percent lower earnings for adult males
and females working in the formal sector (Fishman et al. 2019). Exposure to extreme hot weather during
pregnancy increases the likelihood of Chinese women working in unskilled jobs later in life (Gao et al.
2023). Women who experienced more rainfall as infants during 1953–1974 in Indonesia are taller, com-
pleted more schooling, and are more likely to be wealthier (Maccini and Yang 2009). Quasi-experimental
evidence finds that infants in Ethiopia who lived through the peak of the 1984 drought were significantly
shorter 20 years later, compared to those unaffected by the draught, leading to 5 percent lower annual
income over the course of adulthood (Dercon and Porter 2014). Where climate change decreases food se-
curity, it may result in malnutrition, which may lead to adverse outcomes in later life, including in terms
of education achieved, earnings and labor supply (see, for example, Smith (2009), Gertler et al. (2014),
Attanasio (2015)). Climate change may also impact education directly. In China, total test scores for col-
lege entrance examinations declined by 0.68 percent for a 1 standard deviation increase in temperature
during the exam period (Graff Zivin et al. 2020). Student performance on high-stakes assessment declined
at hot temperature during exam day in the largest public school district in the United States (Park 2022).
Interestingly school air-conditioning can offset these effects (Park et al. 2020). Whether these falls in out-
comes are scarring or can be compensated with later catch up is currently unknown. Evidence from an
earthquake in Pakistan also suggests that among affected children, those with educated mothers catch up
and close the deficits (Andrabi et al. 2021).
32. For example, Somanathan et al. (2021) gather daily data on meters of cloth woven by factory work-
ers. Cai et al. (2018) use number of paper cups produced per factory worker per day.
33. A common approach is to use production data from national accounts divided by country popula-
tion to arrive at per capita value-added, aggregated at the industry level using the International Standard
Industrial Classification (ISIC) (cf. Hsiang 2010).
34. To assess heterogeneity using equation (1), studies interact a dummy variable for highly exposed
industries with the temperature variable or carry out a subsample analysis by industry.
35. The impact of an additional hot day above 95◦ F (35◦ C) for relatively hot regions corresponds to
about a third of losses in colder or milder regions in the United States in terms of labor productivity during
a hotter year.
36. The impact of days with maximum temperature over 100◦ F (38◦ C) among US workers is smaller in
August—typically a hot month—compared to June, suggesting short-term worker acclimatization (Graff
Zivin and Neidell 2014).
37. Hsiang (2010) conjectured that economy-wide output loss in the magnitude of 2.5 percent
for + 1◦ C in the Caribbean is more likely than not driven by labor productivity losses.
38. Future global economic heat related productivity losses have been estimated by a recent system-
atic review of evidence to be in the range of 0.44 percent (RCP 2.6) to 2.9 percent (RCP 8.5) of global GDP
in 2100, after accounting for adaptation (Zhao et al. 2021).
39. See Morton 2007 for a conceptual review of climate and smallholder agriculture.
40. Changes in agricultural outcomes over time in response to weather variability are modest when
compared with, for example, the strong long-term patterns of adaptation in health (see Carleton and
Hsiang 2016 for a review of adaptation in agriculture and health).
Feriga, Lozano Gracia, and Serneels 133
41. Cai et al. (2018) provide an exception. Distributional impacts across levels of income, gender or
ethnicity have received some, but limited, attention in high income countries (Hsiang and Narita 2012;
Hsiang and Jina 2014).
42. Climate migration refers to people changing location in response to the impacts of climate change.
43. Barrett et al. (2023) provide a conceptual review of structural transformation and climate.
44. Climate migration research tends to focus on permanent migration; seasonal or circular migra-
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tion, or commuting time receive much less attention.
45. The study combines a 21-year longitudinal survey data with satellite measurements of climate vari-
ability.
46. Because of the Great Depression, access to capital was limited and growth in local manufacturing
weak, resulting population decline being the main short- and long-term adjustment of the local economy.
47. A 1 percent reduction in precipitation levels is associated with a 0.45 percent rise in urbanization.
Similar evidence exists for sub-Saharan Africa during 1960–2000 with climate migration increasing ur-
ban labor supply, which both strengthened agglomeration economies and reduced wages (Marchiori et
al. 2012).
48. In contrast to other labor themes, the climate migration literature has paid considerable atten-
tion to co-determinants of climate migration, including historical local climate variability, farm income,
wealth, and agricultural yield, as well household member’s education (Saldana-Zorrilla and Sandberg
2009; Feng et al. 2012; Bohra-Mishra et al. 2014; Mueller et al. 2014).
49. While beyond the scope of this paper, it is useful to consider the role of conflict. Estimates of the
impact of climate on migration that do not control for conflict may be mis-interpreted and misinform
policy design. Temperature rise is found to cause conflict, which in turn affects economic performance
and migration. The literature finds a strong relationship between climate change and civil war (Burke et
al. 2009), intergroup conflict (Hsiang et al. 2013) and within country violence and conflict, due to drought
(Maystadt and Ecker 2014) and water scarcity (Unfried et al. 2022) among other reasons. Conflict also
causes migration, especially in low-income countries (Ibáñez and Velez 2008), affects labor supply and
wages (Minoiu and Shemyakina 2014) and physical and human capital (health, educational attainment)
(Serneels and Verpoorten 2015). It disrupts markets and is a key obstacle for economic development and
growth.
50. The distinction between the impact of climate change and the response to it is to some extent
semantic. Workers may respond to higher temperature by reducing their labor supply, which we discussed
under “impact.” Impacts may also be related: reductions in labor supply may lead to sector relocation or
migration.
51. For example, to assess the effectiveness of firms’ climate-control investments, Somanathan et al.
(2021) use an indicator reflecting adoption of air-conditioning as left-hand side variable.
52. Comparing impact across adopter and non-adopter subsamples serves the same objective (cf
Somanathan et al. 2021).
53. For example, Chen and Yang (2019) interact a dummy for high-temperature regions with temper-
ature variables to assess if value added per worker varies by climatic region. Graff Zivin and Neidell (2014)
use this approach to test for adaptation in labor supply response across workers in the hottest and coldest
US counties.
54. This approach has been used to study adaptation in the context of cognitive performance (Graff
Zivin et al. 2018), US corn productivity (Burke and Emerick 2016), and economic growth (Dell et al. 2012).
The estimation typically follows the following specification:
j (t2 −t1 ) + Ci j (t2 −t1 ) +
Yi j (t2 −t1 ) = β Ti e e
Xi j (t2 −t1 ) + γ j (t2 −t1 ) + εi j (t2 −t1 ) (2)
55. The scarcity of rigorous studies is related to the limited availability of high-quality data, as well
as to the relatively late engagement of economics with empirical analysis of climate change (Oswald and
Stern 2019).
134 The World Bank Research Observer, vol. 40, no. 1 (2025)
56. AC adoption can also improve cognitive productivity. In US schools adopting air conditioning, high
school students obtained higher standardized test scores, reducing the impact of high temperature (Park
et al. 2020).
57. The energy-efficient LED lighting attenuates 80 percent of the productivity effects of hot days by
reducing heat dissipation, compared to conventional light bulbs.
58. Whether this adaptive response is optimal remains unclear. While a shift towards large-scale ir-
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rigation in US counties with access to aquifer grounder water established drought resistance, changes
in land allocation toward high-value water-intensive crop increased drought sensitivity (Hornbeck and
Keskin 2014).
59. Crops observed were rice, wheat, maize, sugarcane, groundnut and sorghum.
60. See Dercon (2002) for a detailed examination of household risk-coping strategies.
61. A descriptive World Bank study reports similar findings for MENA (Adoho 2014).
62. See, for example, Coate and Ravallion (1993), Townsend (1994), Udry (1994), and Ambrus et al.
(2014).
63. Carbon pricing puts an explicit price on carbon to curb negative externalities and achieve a social
optimum. It refers to a collection of approaches with carbon taxes and cap-and-trade systems as the most
common (see Auffhammer et al. 2016 for a general discussion of carbon pricing). Carbon pricing is adopted
more in high than middle- and low-income countries, See carbonpricingdashboard.worldbank.org. for an
overview of established and ongoing carbon pricing initiatives worldwide.
64. This reflects the cross elasticity of Full Time Equivalent (FTE) with respect to real electricity prices.
This analysis, like most US analysis, uses within-state, year-to-year variation in US electricity prices, as a
proxy for higher energy prices resulting from carbon pricing policy, based on the reasoning that electricity
prices are a first-order impact channel of climate policy on labor market. While this is a well-reasoned ap-
proach, one cannot exclude the potential for omitted variable bias, for instance, if unobservables influence
changes over time in both within-state electricity prices and labor outcomes.
65. To put the aggregate estimates in context, a forward-looking analysis vis-a-vis the predicted US
national carbon emission targets indicates a short-term loss of 460,000 FTE employment, or 0.6 percent
of total employment, if electricity prices rise by 4 percent. These estimates do not account for the effects
of compensatory measures that may offset the negative impact on labor demand.
66. The $10/ton CO2 tax led to -38 percent employment in chemical manufacturing, -25 percent in
electric power generation, +18 percent in healthcare services, and + 15 percent in retail trade.
67. A green job can be defined from an output-based or a process-based perspective, where the former
focuses on environmental goods and services while the latter concentrates on production processes (US
Bureau of Labor Statistics). The use of multiple definitions for green jobs complicates comparison across
studies (Deschenes 2013).
68. The paper identifies impact by exploiting geographical variation in regulatory stringency across
the United States. An ILO descriptive analysis that assesses the skills for green transition in 32 countries
worldwide, estimates that some 2 million workers may require “re-skilling” for different occupations, and
close to 20 million require “upskilling” for new jobs, across low-, medium-, and high-skilled occupations
(ILO 2019). Changes in required skills—whether by greening existing jobs or developing new “green” jobs—
are concentrated in renewable energy, environmental goods and services, and construction; effects of the
low-carbon transition are less certain for other sectors. Gender disparity is expected to persist, with most
job creation and reallocation likely to occur in men-dominated mid-skill occupations.
69. The expansion of road networks is found to have a significant causal impact. It enabled the inte-
gration of local labor markets in post-liberalization India (Allen and Atkin 2022).
70. Improved labor mobility can also improve general wellbeing, including health. Over the last 30
years, 4–7 percent of total life expectancy gains in the United States are attributable to the geographical
mobility of the US population from the cold Northeast to the warm Southeast regions (Deschenes and
Moretti 2009).
71. For a discussion of climate change and land and the challenges for banking and the financial sector
at large, see IPCC (2019) and Campiglio et al. (2018), respectively.
Feriga, Lozano Gracia, and Serneels 135
72. Draft results of a companion study that takes stock of existing evidence for the Middle East and
North Africa are available upon request.
73. The overview builds on one possible categorization of climate change policies, used by the Foreign,
Commonwealth, and Development Office (FCDO) UK, among others.
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Appendix A1: Climate Change Policies
Many of the existing policy responses worldwide primarily aim to reduce emissions
and their negative impacts, but they rarely explicitly target labor outcomes. Table A1.1
presents examples of key climate change policy interventions that have undergone
evaluation.73
Table A1.1. Examples of General Climate Change Policy Interventions
Policy area Example of intervention Reference
Mitigation
Carbon price . Carbon tax and emission trading system World Bank, IPCC (2014)
Fossil fuel subsidy . Reduce fossil fuel subsidy OECD (2018), IPCC (2014)
reform
Power generation . Shift from coal to renewables using pricing Lo (2014), World Bank Scaling Solar
. Supporting governments to transit out of
coal and develop alternatives to diesel
Agriculture, . Payment for ecosystem services Jayachandran et al. (2017), Mohebalian
forestry, land use and Aguilar (2018)
Transport and . Infrastructure and urban design IPCC (2014), New Climate Economy
urban . Low-carbon vehicles and high-volume public (2016)
transport
. R&D and deployment of energy efficient
technologies
End use . Nudges to encourage use of more efficient Mobarak (2012), Leach and Oduro
appliances (2015), Adhvaryu et al. (2020)
. Development of evidence on what works in
reducing traditional biomass use
. Energy efficiency products and standards
Adaptation
Crop varieties . New high yield crop varieties tolerant to Dar et al. (2013)
pests/diseases and drought/flood/salinity
Disaster . Early warning systems, enhanced weather Kuik et al. (2016)
preparedness and climate services, flood prevention and
protection, preventative health for climate
sensitive diseases
Adaptive social . Shock-responsive adaptation Conway and Schipper (2011)
protection . Alignment with humanitarian systems
Feriga, Lozano Gracia, and Serneels 145
Appendix A2: Mapping the Evidence
Table A2.1 provides a stylized overview visualizing what evidence exists on the im-
pact of climate change on labor.
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Table A2.1 Visualization of Existing Evidence on Climate Impacts on Labor, Globally
Area of impact Climate related events
Temperature Tropical
and heat Sea level rise cyclones and
extremes precipitation and flooding storms Drought
Economy wide E E N E N
Agriculture E E N E E
Industry and services E E N E N
Demand for labor, by sector
Agriculture E E N N E
Industry outside agriculture E N N N N
Energy∗ E – – – –
Service N N N N N
Labor supply and time allocation E E N N N
Worker productivity on-the-job E N N N N
Income, vulnerability among N N N N N
self-employed
Reallocation of labor
Sectoral reallocation E E N N N
Climate migration E E E N N
E = Evidence across context, mostly from historical analysis or from simulation; N = No or very limited quantitative
evidence;
∗ Work on Energy mostly concentrates on the impact of green transition rather than climate change events themselves.
The World Bank Research Observer
© 2024 International Bank for Reconstruction and Development / The World Bank. Published by Oxford University Press
https://doi.org/10.1093/wbro/lkae002
146 The World Bank Research Observer, vol. 40, no. 1 (2025)