A Macro Study of the Unequal Effects of Climate Change

Layer 1: Overview

This paper develops a macro heterogeneous-agent model to quantify the distributional welfare impacts of higher temperatures from climate change across income groups in the United States. The motivation is that existing macro climate-economy models either abstract from heterogeneity entirely or focus on spatial heterogeneity across regions rather than income heterogeneity within regions. The paper fills this gap by modeling how the welfare consequences of temperature change depend on both the region a household lives in and its position in the income distribution.

The model is calibrated to the US using five data sources: NIPA accounts from the BEA (averaged 1997–2020), the 2015 Residential Energy and Consumption Survey (RECS), PRISM climate data (1950–2022), a proprietary product-level data set of over 1,000 heaters, air conditioners, and heat pumps scraped from ecomfort.com in fall 2023, and county-level climate projections for year 2100 under RCP 8.5 from Rasmussen et al. (2016). The US is divided into five regions (cold, cool, mild, warm, and hot) of approximately equal population based on average county temperature. The quantitative exercise compares two stationary equilibria: a contemporary equilibrium using the current temperature distribution and a climate-change equilibrium using the projected 2100 distribution under RCP 8.5 (a no-large-scale-climate-policy scenario). Welfare is measured using the consumption-housing equivalent variation (CHEV), defined as the percent increase in consumption and housing a household would require in every period in the contemporary equilibrium to be indifferent between the two equilibria.

Households adapt to temperature through two channels: an intensive margin (adjusting energy use for heating and cooling given existing equipment) and an extensive margin (deciding whether to purchase a heater, air conditioner, or heat pump, each carrying a fixed cost). The production functions for heating and cooling are estimated by OLS on the product-level data set, yielding equipment exponents of 0.35 (air conditioners), 0.28 (heaters), and 0.27 (heat pumps), and energy exponents of 0.77, 0.86, and 0.85, respectively, with R-squared values of 0.97, 0.79, and 1.00. A key analytical insight from a stylized model is that the outdoor temperature acts as a “transfer from nature” to households — warmer days in cold weather and cooler days in hot weather reduce the energy households must purchase, augmenting real income. Because this transfer is a larger share of income for lower-income households, its changes are distributionally regressive when the transfer falls (hotter regions warming further) and progressive when it rises (colder regions warming).

The main quantitative findings are as follows. Among middle- and high-income households, climate change generates progressive welfare gains in colder regions — ranging from +0.71 percent of consumption-and-housing for households in the third income decile in the cool region to near-zero for the highest income households — and regressive welfare losses in hotter regions, ranging from −1.85 percent for third-decile households in the warm region to near-zero for high-income households. These patterns are driven by the intensive margin (changes in transfers from nature). For low-income households, the pattern reverses: low-income households in colder regions suffer welfare losses (the dominant effect is that climate change forces them to purchase their first air conditioner), while some low-income households in hotter regions experience welfare gains (they can forgo purchasing a heater). Climate change raises the Gini coefficient on lifetime welfare by 1.02, 1.01, and 0.50 percent in the cold, cool, and mild regions, and reduces it by 0.09 and 0.21 percent in the warm and hot regions. Aggregate welfare effects from the heterogeneous-agent model substantially exceed what a representative-agent model would imply: for example, in the mild region, climate change reduces aggregate welfare by 0.65 percent in the baseline but only 0.17 percent in the representative-agent version.

Policy experiments reveal: (1) Fully offsetting the welfare costs of climate change for the lowest-income households would require government spending on energy assistance to more than double (a factor of 2.2 increase), with the largest increases concentrated in colder regions. (2) A universal heat-pump mandate eliminates the extensive-margin channel, producing monotonically progressive welfare gains in colder regions and monotonically regressive welfare losses in hotter regions across all income deciles. (3) Heat-pump cost parity with heaters largely increases adoption and moderates welfare costs, but low-income households in the hot region see limited improvement because they still prefer air conditioners. (4) Accounting for temperature effects on the labor productivity of outdoor workers (roughly 8 percent of the workforce, concentrated at lower incomes) amplifies welfare costs in hotter regions and moderates them in colder regions, with magnitudes tied to the share of workers affected.

Layer 2: Deep Dive

What is the identification strategy, and what are the main threats to it?

The paper is a calibrated structural model rather than an empirical identification exercise. Identification in the sense of parameter estimation comes from two sources: (1) OLS estimation of heating and cooling production functions on cross-sectional product-level data, where manufacturers measure capacity and efficiency under standardized conditions, limiting TFP endogeneity concerns that plague aggregate production function estimation; and (2) internal calibration of remaining parameters to match a set of moments from RECS 2015 and NIPA. Threats to the structural analysis include the assumption that households treat housing and equipment as flow (rental) choices rather than durable stocks, abstracting from switching costs and adjustment costs over the transition — the paper explicitly notes this limits the analysis to long-run stationary equilibria. The small-open-economy assumption for capital removes domestic capital-market clearing as a constraint. The calibration uses 2015 RECS (not 2020) to avoid COVID-19 distortions to cooling budget shares. The paper abstracts from amenity values of outdoor temperature, mortality from temperature exposure (approximately 0.04 percent of US deaths from 1999–2020), and spatial migration responses.

What are the two core mechanisms and how are they distinguished?

The two mechanisms are the intensive margin (how much energy to use given existing equipment) and the extensive margin (whether to purchase heating or cooling equipment at all). The paper distinguishes them analytically using the simple model, which isolates the intensive margin by assuming all households have equipment. The intuition from the simple model — outdoor temperature as a transfer from nature — explains why welfare effects are progressive in regions where climate change makes temperatures more moderate (transfers rise) and regressive where temperatures become more extreme (transfers fall). The extensive margin is then added in the quantitative model through fixed costs of heater, air conditioner, and heat pump equipment. The paper shows that climate change affects specialization favorability (the degree to which a temperature distribution favors concentrating on only heating or only cooling equipment), and that this extensive-margin channel is most important for lower-income households who are near a corner solution of specializing in only one type of equipment. The heat-pump-mandate counterfactual is used to isolate the intensive-margin channel: when all households use heat pumps in both equilibria, the extensive-margin decision is unchanged by climate change, and all welfare effects are driven purely by transfers from nature.

What heterogeneity is documented across income groups and regions?

Welfare effects vary dramatically in both sign and magnitude. Among middle- and high-income households, climate change generates progressive welfare gains in colder regions (e.g., +0.71 percent CHEV for third-decile households in the cool region, falling toward zero at the top) and regressive welfare losses in hotter regions (e.g., −1.85 percent CHEV for third-decile households in the warm region, again near-zero at the top). For low-income households, the pattern reverses: they experience welfare losses in colder regions (forced to buy first air conditioner) and welfare gains or smaller losses in hotter regions (can forgo purchasing a heater). Figure 2 in the paper shows these crossing patterns by income decile for all five regions simultaneously. The Gini coefficient changes by +1.02% (cold), +1.01% (cool), +0.50% (mild), −0.09% (warm), and −0.21% (hot). Migration incentives also differ: high-income households gain incentives to move to cooler regions (driven by transfers from nature), while low-income households gain incentives to move to warmer regions (driven by specialization changes).

What is the ’transfers from nature’ concept and why does it produce differential welfare effects?

The paper formalizes the idea that outdoor temperature provides free heating or cooling that substitutes for costly purchased energy. On a cold day with outdoor temperature ζ, nature provides ζ degrees of heating for free, effectively augmenting household income by p_eh * ζ (the value of that heating at market prices). This transfer is identical in absolute terms for all households regardless of income, but it is a larger fraction of income for low-income households, so its loss or gain has greater proportional welfare impact on them. This parallels the progressivity of lump-sum transfers in public finance: losing a dollar matters more when income is lower. Consequently, when climate change moves a region to more moderate temperatures (colder regions), the resulting increase in transfers from nature is progressive — lower-income households gain proportionally more. When climate change moves a region to more extreme temperatures (hotter regions), the decrease in transfers is regressive — lower-income households lose proportionally more. The amenity value of outdoor temperature (distinct from the heating/cooling transfer) is abstracted from in the quantitative model on the grounds that, per the simple model, it does not affect the cross-income distribution of welfare changes if preferences over amenities are uncorrelated with income.

How does the extensive margin generate the reversal of welfare effects for low-income households?

The extensive margin works through what the paper calls ‘specialization favorability.’ When a temperature distribution is dominated by cold days, households can optimally purchase only heater equipment, avoiding the additional fixed cost of an air conditioner; the reverse holds in hot climates. Climate change reduces the specialization favorability index in colder regions by adding more hot days, and increases it in hotter regions by reducing cold days. The welfare impact of moving between a corner solution (one type of equipment) and an interior solution (two types of equipment, or a heat pump) tends to be larger than moving between two interior solutions. In the cold region, climate change causes the majority of households in the bottom three income deciles to transition from not having air conditioning to having it (Figure 5, left panel). The fixed cost of buying an air conditioner for the first time exceeds the intensive-margin gains from more moderate temperatures, producing net welfare losses. In the hot region, many second-through-fourth decile households move from having heat in the contemporary equilibrium to not having heat in the climate-change equilibrium (Figure 5, right panel), saving the fixed cost and producing net welfare gains despite more extreme temperatures.

How is the model calibrated and what is the quality of fit?

Externally calibrated parameters include: capital income share α = 0.26 (Kiyotaki et al., 2011), depreciation rate δ = 0.066, interest rate r* = 0.04, CRRA coefficient σ = 2, bliss point temperature ζ* = 18°C, labor productivity process (ρ = 0.97, σ²_ε = 0.02, σ²_ξ = 0.66 from Kaplan, 2012), and production function exponents estimated from the ecomfort.com data. Internally calibrated parameters are jointly chosen to match: wealth-to-output ratio (3.0), housing-to-non-housing capital ratio (0.88), average heating budget share for non-heat-pump households (0.014), average cooling budget share (0.0055), energy budget share for heat-pump households (0.014), fractions of households with heating (0.95), cooling (0.86), and heat pumps (0.09), the ratio of energy budget shares between the fifth and first income quintile (0.12), the ratio of energy expenditures between high and low income (1.72), and energy assistance as a fraction of energy expenditures (0.83). Table 3 shows the model matches all targeted moments closely. External validation (untargeted moments) shows the model also replicates the associations between heating/cooling degree days and budget shares, equipment ownership, and indoor temperature choices, with similar signs and magnitudes to RECS 2015 data. One limitation is that the model overstates heat pump adoption (17% in model vs. 9% in 2015 RECS, though 14% in 2020 RECS), because it treats modern cold-weather-capable heat pumps as the default.

What do the policy counterfactuals show?

Four policy experiments are analyzed. First, scaling energy assistance proportionally to energy needs under climate change reduces assistance by 24% in cold and 20% in cool regions (where transfers from nature increase) and raises it by 9%, 36%, and 79% in mild, warm, and hot regions. Government spending increases by 25%, but the program remains smaller than 0.02% of output. This scaling partially offsets but does not eliminate the distributional distortions. Fully eliminating welfare costs for the lowest-income households would require multiplying energy assistance spending by a factor of 2.2. Second, a universal heat-pump mandate (analogous to natural gas bans like New York, Washington DC, or California’s post-2030 ban on natural gas furnaces) eliminates all extensive-margin effects because all households hold heat pumps in both equilibria. Under this mandate, climate change produces monotonically progressive welfare gains across all income groups in colder regions and monotonically regressive welfare costs in hotter regions. Third, heat-pump cost parity with heaters drives near-universal heat pump adoption and broadly moderates welfare costs relative to baseline, but the lowest-income households in the hot region see limited improvement because they still prefer air conditioners over heat pumps even at cost parity (air conditioners are cheaper and heat pumps’ heating advantage is less valuable in an already-hot, increasingly-hotter climate). Fourth, the labor productivity extension (using the Richardson construction cost database adjustment factor of 1% per degree outside 40°F–85°F) implies that climate change raises low-income productivity by 2% in cold and 0.9% in cool regions and reduces it by 0.1%, 1.1%, and 2.2% in mild, warm, and hot regions. These labor-productivity changes modestly moderate welfare costs in colder regions and amplify them in hotter regions for low-income households.

Why does income heterogeneity matter for aggregate welfare calculations?

The paper demonstrates that a representative-agent model substantially underestimates the aggregate welfare cost of climate change in all regions except the hot region. In the cold region, the aggregate CHEV is −1.03% in the baseline but the average (seventh-decile) household experiences small positive welfare effects (+0.19%), and the representative-agent model yields −0.00%. In the mild region, the aggregate is −0.65% but the representative-agent model gives −0.17%. The discrepancy arises because the welfare distribution is skewed: large losses for low-income households in colder regions are not offset by small or negative gains for high-income households, so the average is dominated by the tails. In the hot region the direction reverses: the baseline aggregate benefit (+0.24%) is driven by large gains at the bottom that the representative-agent model (−0.43%) misses entirely. This finding parallels the broader macroeconomics literature showing that income heterogeneity affects the aggregate welfare cost of business cycles, inflation, and asset pricing.

How does this paper relate to and differ from prior work?

The paper sits at the intersection of two literatures. The macro climate-economy literature (Acemoglu et al., 2012; Golosov et al., 2014; Barrage, 2020) typically uses representative-agent models that abstract from heterogeneity. The spatial heterogeneity literature (Cruz and Rossi-Hansberg, 2024; Bilal and Rossi-Hansberg, 2023; Rudik et al., 2022) studies how welfare consequences vary across regions based on their income levels and exposures but not within-region income differences. The within-region inequality literature (Dennig et al., 2015; Kornek et al., 2021; Belfori and Macera, 2022; Douenne et al., 2023) adds heterogeneous fixed income types to integrated assessment models, but does not model endogenous income and wealth distributions. Blanz (2023) is the closest precursor: it uses a standard incomplete-markets model to study food-price effects of climate change in developing countries, but does not model the temperature-equipment-energy production technology. The empirical literature (Hsiang et al., 2017; Park et al., 2018; Doremus et al., 2022) estimates reduced-form relationships between temperature and energy spending by income group, but cannot decompose intensive vs. extensive margin mechanisms or conduct structural policy counterfactuals. The key novel contributions are: (1) endogenous income and wealth heterogeneity within the Bewley-Huggett-Aiyagari tradition, (2) explicit modeling of both margins of temperature adaptation with estimated production functions, and (3) the ability to separately identify the roles of transfers from nature and specialization favorability.

What robustness checks are conducted?

The paper reports several robustness checks. First, the main calibration uses the housing exponent γ = 0.1, but Appendix Figure D.1 shows results with γ = 0.4 (the upper bound implied by the RECS regression of energy on square footage, before controlling for quality), finding broadly similar qualitative results. Second, the 2015 RECS is used instead of the 2020 RECS due to COVID-19 distortions to cooling budget shares; the paper notes heating budget shares are similar between the two surveys while cooling shares are materially higher in 2020. Third, external validation of the model on untargeted moments (associations between HDD/CDD and heating/cooling budget shares, equipment ownership, and indoor temperatures) confirms the model’s predictive validity. Fourth, the welfare results are computed for both the main five-region model and a representative-agent version, documenting the magnitude of the aggregation bias. Fifth, the labor productivity extension bounds the relevant population (bottom 3% vs. bottom 16% of workers) to bracket the Occupational Requirements Survey estimate of 8% of workers constantly or frequently exposed outdoors.

What are the scope conditions and limitations of the main results?

Several important scope conditions apply. The analysis focuses exclusively on the direct effects of higher temperatures in the US; it does not cover other forms of climate damage (sea level rise, storm frequency, drought, wildfire) or effects in other countries. The model is solved for stationary equilibria, so it cannot speak to transition dynamics or the welfare costs of adjustment during the period when households are switching equipment. Housing and equipment are modeled as flow (rental) choices, abstracting from switching costs, adjustment frictions, and the interaction between homeownership and equipment decisions. The model abstracts from the amenity value of outdoor temperature (e.g., preference for pleasant weather), temperature-related mortality (about 0.04% of US deaths, 1999–2020, heavily concentrated among the unhoused population outside the model), and behavioral adaptation beyond energy and equipment choices (migration is analyzed only as a partial equilibrium incentive calculation, not as an equilibrium outcome). The capital market operates as a small open economy, so general equilibrium effects on interest rates are absent. Labor productivity effects of temperature are only explored for low-income workers in the outdoor sector, not for higher-income or indoor workers.

What are the migration findings and their caveats?

The paper shows that climate change increases incentives for high-income households to migrate to cooler regions (driven by the transfers-from-nature channel — cooler regions offer larger increases in transfers) and increases incentives for low-income households to migrate to warmer regions (driven by the specialization channel — warmer regions allow forgoing heater equipment). The magnitude of the change in migratory pressure for high-income households is much smaller (order of magnitude roughly 0.15 on the paper’s scale) than for low-income households (order of magnitude roughly 3 on the same scale). The authors explicitly caveat that this is a partial equilibrium exercise: the model abstracts from the amenity value of temperature (which would reduce pressure to move to warmer regions by reducing the attractiveness of hot destinations) and from other dimensions of climate change (storm risk, fire risk) that would affect migration incentives independently.

Key Concepts

Transfers from nature: In this paper’s framework, outdoor temperature acts as a subsidy equivalent to income: on a cold day, nature provides degrees of heating for free, augmenting household real income by the value of that heating energy; on a hot day, it provides degrees of cooling. The transfer is the same in absolute terms for all households but represents a larger fraction of income for lower-income households, making changes in temperature distributionally progressive (when transfers rise) or regressive (when transfers fall).

Extensive margin of temperature adaptation: The binary decision of whether to purchase temperature-control equipment — a heater, air conditioner, or heat pump — each carrying a fixed cost. Households at the extensive margin may optimally forego one type of equipment entirely (complete specialization), and climate change can force them to acquire equipment they previously lacked or allow them to drop equipment they previously held.

Intensive margin of temperature adaptation: The continuous decision of how much energy to purchase to operate existing heating and cooling equipment in order to achieve a desired indoor temperature, conditional on having that equipment. Changes in the outdoor temperature distribution affect energy expenditures along this margin for all households that already own equipment.

Specialization favorability index: A region-level index S_n ∈ [0,1] defined as the absolute difference between total degrees of heating need and total degrees of cooling need, divided by their sum. Higher values indicate that the temperature distribution is more dominated by either heating or cooling demand, making it more efficient for households to specialize in a single type of temperature-control equipment rather than purchasing both. Climate change reduces specialization favorability in colder regions and increases it in hotter regions.

Consumption-housing equivalent variation (CHEV): The paper’s welfare metric: the percentage by which a household’s consumption and housing would need to increase in every period of the contemporary equilibrium for the household to be indifferent between remaining in the contemporary equilibrium and living in the climate-change equilibrium. Negative CHEV values indicate welfare losses from climate change.

Temperature damage function D(T): A function mapping the deviation of indoor temperature from the bliss point to the fraction of full utility the household receives from housing services. D equals 1 when indoor temperature equals the bliss point (18°C in calibration) and falls below 1 as indoor temperature deviates in either direction, with the rate of decline governed by parameter χ. This function creates the motive to use energy for heating and cooling.

RCP 8.5: As used in this paper, a climate scenario from the CMIP archive representing emissions in the absence of large-scale climate policy, used to construct the 2100 temperature distribution in the climate-change equilibrium. County-level projections come from Rasmussen et al. (2016), probability-weighted across climate models.