Effects of climate change on combined labour productivity and supply: an empirical, multi-model study

Abstract

Background Although effects on labour is one of the most tangible and attributable climate impact, our quantification of these effects is insufficient and based on weak methodologies. Partly, this gap is due to the inability to resolve different impact channels, such as changes in time allocation (labour supply) and slowdown of work (labour productivity). Explicitly resolving those in a multi-model inter-comparison framework can help to improve estimates of the effects of climate change on labour effectiveness.Methods In this empirical, multi-model study, we used a large collection of micro-survey data aggregated to subnational regions across the world to estimate new, robust global and regional temperature and wet-bulb globe temperature exposure-response functions (ERFs) for labour supply. We then assessed the uncertainty in existing labour productivity response functions and derived an augmented mean function. Finally, we combined these two dimensions of labour into a single compound metric (effective labour effects). This combined measure allowed us to estimate the effect of future climate change on both the number of hours worked and on the productivity of workers during their working hours under 1•5°C, 2•0°C, and 3•0°C of global warming. We separately analysed low-exposure (indoors or outdoors in the shade) and high-exposure (outdoor in the sun) sectors.Findings We found differentiated empirical regional and sectoral ERF's for labour supply. Current climate conditions already negatively affect labour effectiveness, particularly in tropical countries. Future climate change will reduce global total labour in the low-exposure sectors by 18 percentage points (range -48•8 to 5•3) under a scenario of 3•0°C warming (24•8 percentage points in the high-exposure sectors). The reductions will be 25•9 percentage points (-48•8 to 2•7) in Africa, 18•6 percentage points (-33•6 to 5•3) in Asia, and 10•4 percentage points (-35•0 to 2•6) in the Americas in the low-exposure sectors. These regional effects are projected to be substantially higher for labour outdoors in full sunlight compared with indoors (or outdoors in the shade) with the average reductions in total labour projected to be 32•8 percentage points (-66•3 to 1•6) in Africa, 25•0 percentage points (-66•3 to 7•0) in Asia, and 16•7 percentage points (-45•5 to 4•4) in the Americas.Interpretation Both labour supply and productivity are projected to decrease under future climate change in most parts of the world, and particularly in tropical regions. Parts of sub-Saharan Africa, south Asia, and southeast Asia are at highest risk under future warming scenarios. The heterogeneous regional response functions suggest that it is necessary to move away from one-size-fits-all response functions to investigate the climate effect on labour. Our findings imply income and distributional consequences in terms of increased inequality and poverty, especially in low-income countries, where the labour effects are projected to be high. Funding COST (European Cooperation in Science and T...

Explicit Calculations of Wet‐Bulb Globe Temperature Compared With Approximations and Why It Matters for Labor Productivity

“…We also compare annual labor productivity estimated from “WBGT id plus 3°C” with that from Liljegren's WBGT formulation (Figure S13 in Supporting Information S1). Here, the constant correction factor is added to both daytime and nighttime hours in order to be consistent with previous studies that added 3°C to daily WBGT id without differentiating daytime and nighttime (Dasgupta et al., 2021; Kjellstrom et al., 2014; Szewczyk et al., 2021). “WBGT id plus 3°C” overestimates labor loss by 5 to >10% over the tropics (Figure S13 in Supporting Information S1).…”

“…Some studies adopted indoor WBGT approximations (WBGT id = 0.7* T pwb + 0.3* T a ) to outdoors (Dunne et al., 2013; Knutson & Ploshay, 2016; Li et al., 2017; C. Li, Sun, et al., 2020; D. Li, Yuan, & Kopp, 2020; Newth & Gunasekera, 2018; Schwingshackl et al., 2021), which misses the effects of solar radiation and wind. There have been attempts to correct this missing by adding a constant (e.g., 3°C) to WBGT id to represent the effects of solar radiation (Dasgupta et al., 2021; Kjellstrom et al., 2014; Szewczyk et al., 2021), the validity of which however has not been tested.…”

“…Li, Yuan, & Kopp, 2020;Newth & Gunasekera, 2018;Schwingshackl et al, 2021), which misses the effects of solar radiation and wind. There have been attempts to correct this missing by adding a constant (e.g., 3°C) to WBGT id to represent the effects of solar radiation (Dasgupta et al, 2021;Kjellstrom et al, 2014;Szewczyk et al, 2021), the validity of which however has not been tested.…”

“…This simplified form neglects the effects of solar radiation and wind speed making it only apply to indoor or well‐shaded thermal conditions. Some other studies apply indoor or shaded WBGT (WBGT id ) approximations to outdoors by adding a constant factor (e.g., 3°C) to represent the effects of solar radiation (Dasgupta et al., 2021; Kjellstrom et al., 2014; Szewczyk et al., 2021), the validity of which has not been tested.…”

The Labor Productivity Consequences of Exposure to Particulate Matters: Evidence from a Chinese National Panel Survey

“…As we discussed above, we include the same set of control variables in both stages of the estimation, include variables for 5 °C temperature bins, second-order polynomials in average snow thickness, vapor pressure, wind speed, sunshine duration, relative humidity, cumulative precipitation, rain duration, and snow duration. These local weather shocks have known effects on labor supply and productivity (e.g., [ 49 , 50 ]). Controls also include time-varying individual characteristics: retirement status and marital status, noting that any time-invariant individual characteristics will be absorbed by our individual fixed effect.…”