Carbon Pricing and Inequality: A Normative Perspective

Layer 1: Overview

This paper quantifies the sources and distributional consequences of unexpected carbon price changes for European households using a money-metric welfare framework. The motivation is stark: while carbon taxes enjoy broad support among economists, they face persistent public opposition — exemplified by Australia’s 2014 repeal, France’s 2018 Yellow Vest protests, and the 2025 rollback of Canada’s consumer carbon tax. The authors ask whether average welfare losses are unusually large, and whether the burden falls disproportionately on vulnerable groups, both questions with direct implications for understanding and reducing political resistance.

The empirical approach rests on the “feasible set approach” of Del Canto et al. (2025), which applies the Envelope Theorem to show that the first-order welfare impact of a shock on any household is fully summarized by how the shock changes the discounted present value of their future budget sets — through consumption-basket prices, labor income, financial wealth (asset prices and dividends), and government transfers. This money-metric welfare change is preference-free up to first order: behavioral responses drop out, and the measure is independent of specific utility-function assumptions. The framework is appropriate for policy shocks (supply-side) but not for preference shocks.

The geographic focus is euro-area countries (excluding the Netherlands and Austria due to data gaps) over 1999–2019. The identification strategy follows Känzig (2023): high-frequency shifts in EU ETS carbon futures prices around regulatory events affecting allowance supply are used as instruments in an external-instruments VAR to isolate plausibly exogenous carbon policy shocks. These shocks are then projected onto a wide array of household-level outcomes using local projections (Jordà 2005). The normalization throughout is a 1% increase in the HICP energy component on impact, which corresponds to roughly a 2.5-euro (or about 20%) increase in EU ETS carbon prices. Cross-sectional household budget data come from three Eurostat/ECB surveys: the Household Budget Survey (HBS, 2015 wave) for consumption baskets, EU-SILC (from 2004) for labor and transfer income by demographic group, and the Household Finance and Consumption Survey (HFCS) for household portfolio positions. Demographics are grouped by four age brackets (25–34, 35–49, 50–64, 65+), two education levels (college vs. non-college), three income brackets (bottom quartile = low, middle 50% = mid, top quartile = high), and four geographic regions (Southern, Western, Northern, Eastern Europe).

The main quantitative findings are as follows. First, aggregate welfare losses are large: a 1% carbon-policy-induced energy price increase causes an average welfare loss of approximately 250 euros, corresponding to about 0.5% of a household’s three-year consumption (68% confidence band: 0.06% to 0.94%). Second, decomposing by channel, the direct consumption-price effect accounts for 0.19% of three-year consumption (68% CI: 0.02% to 0.35%); the labor income channel for 0.43% (68% CI: –0.08% to 0.93%); the portfolio channel for –0.04% (a welfare gain; 68% CI: –0.10% to 0.01%); and the transfer income channel for –0.07% (a welfare gain; 68% CI: –0.15% to 0.02%). Labor income is thus the dominant driver — both in aggregate and in the distributional patterns.

Third, distributional heterogeneity is pervasive and statistically significant (joint F-tests reject uniformity with p-value = 0.00 across all demographic groupings). Non-college-educated households bear welfare losses of roughly 0.6% of three-year consumption, versus roughly 0.3% for college graduates — a gap concentrated in the labor income channel, not the consumption channel (which is broadly similar across groups at around 0.2%). By income, the pattern is U-shaped: young, low-income households suffer the largest losses, exceeding 1% of three-year consumption, while middle-income and older households are the most insulated; high-income households also experience significant losses (around the 0.5% average), driven by their own labor income exposure. Households aged 65 and over suffer welfare losses of only around 0.15%, largely because they are retired from the labor market.

Fourth, regional heterogeneity is stark. Southern Europe bears the highest burden, with welfare losses of 0.5% to 0.8% for working-age households; Eastern Europe also faces substantial losses; Western Europe stands at around 0.2% to 0.3%; Northern Europe is the most insulated, with losses below 0.2% and not statistically significant. The labor income channel is the primary driver of these regional differences, consistent with more rigid labor markets in Southern and Eastern Europe (stronger employment protection, less flexible wage-setting). Northern Europe is protected partly by its high share of renewable energy, which mutes the carbon-price pass-through. Eastern Europe benefited from disproportionate free ETS allowance allocations over the sample period, dampening direct price impacts.

These results collectively suggest that public opposition to carbon taxes may stem from legitimate distributional concerns rather than mere ideological resistance or ignorance. The authors conclude with three policy implications: (1) compensation schemes focused only on consumption prices will be insufficient because the dominant channel is labor income; (2) expansionary (green) monetary policy could ease the income burden, though at some inflationary cost; and (3) redistribution should run from older to younger households, since working-age groups bear the disproportionate burden while retirees are largely insulated.

Layer 2: Deep Dive

What is the identification strategy for the carbon policy shock, and what are the main threats to identification?

The instrument is the high-frequency shift in EU ETS carbon futures prices around regulatory events affecting allowance supply (following Känzig 2023). The logic is that economic conditions are already priced in prior to the regulatory news, so futures-price movements in a tight window around those events reflect only policy surprises. This instrument is then used in an external-instruments VAR to identify a monthly structural carbon policy shock series (1999–2019). The local projections use 6 lags for monthly outcomes and 2 lags for quarterly outcomes, plus a linear trend and a dummy for the euro sovereign debt crisis (July 2011–March 2012). The main identification threats are: (a) if economic conditions are not fully priced into carbon futures before the regulatory events, the instrument could be correlated with macroeconomic conditions; (b) the framework assumes no preference shocks, which rules out COVID-style demand shifts; (c) the small-noise approximation underlying the feasible-set approach is less suitable for large aggregate shocks.

Why does the feasible-set approach not require specific preference assumptions, and what are its limitations?

By the Envelope Theorem applied to household optimization, first-order welfare effects depend only on how the policy changes the prices and quantities in the household’s budget constraint — not on how preferences are shaped. Behavioral responses drop out at first order. The welfare metric is money-metric: the willingness-to-pay to avoid the shock, expressed in euros (income units). Limitations: (1) It is a small-noise approximation around a zero-risk limit; large aggregate shocks are not well-handled. (2) It is valid for shocks from the production or policy side but not for preference shocks (e.g., discount rate changes). (3) Accounting properly for idiosyncratic risk requires covariance weights (Theta terms in Proposition 1 of the appendix); Del Canto et al. (2025) estimate these at –0.1 to –0.4, implying somewhat attenuated welfare levels but no meaningful change to the distributional comparisons. (4) Carbon emissions-reduction benefits are excluded from the welfare calculation by design, since the paper focuses on the pecuniary costs side only.

What is the mechanism behind the labor income channel, and how does it vary across demographic groups and regions?

Carbon price increases raise production costs for energy-intensive sectors, reduce output and employment, and depress aggregate wages — a general equilibrium effect that transmits to household labor income over multiple quarters. The average labor income response peaks at around 1% below trend. For non-college-educated households the peak fall exceeds 1%, while for college graduates the response is more muted. By income group, low-income households face the sharpest falls — around 2–4% over the three-year horizon — whereas middle-income households fall by approximately 0.5–1% and high-income households by about 1%. These effects are larger than those estimated by Del Canto et al. (2025) for oil price shocks on US households (approximately 0.3% welfare loss from labor income after a 10% oil price increase), which the authors attribute to more rigid European labor markets: strong employment protection limits wage cuts but discourages hiring and prolongs unemployment spells, amplifying extensive-margin adjustments. In Southern and Eastern Europe, rigidities are most pronounced, generating the largest regional labor-income responses. Northern and Western Europe show more muted responses.

What is the role of the portfolio channel, and who gains or loses through it?

Stock prices fall by a peak of about 5% and dividends decline by about 3% after a carbon policy shock. Bond prices initially decline then partially recover. House prices decline substantially but with a lag. The welfare effect of asset price changes depends on whether a household is a net buyer or net seller of the asset. Younger households in the accumulation phase gain from falling asset prices (they can buy cheaply); older households planning to dis-save lose. The portfolio channel is quantitatively modest: average welfare gain of about 0.04%, most pronounced for younger college-educated households. The channel is not large enough to offset labor income or consumption-price losses for any group.

What is the role of the transfer income channel, and which groups benefit most?

Transfer income — which the paper splits into inflation-indexed pension income and other government transfers (unemployment, sickness, disability, education benefits) — generates a welfare gain of about 0.07% on average. Pensions are indexed to inflation and rise as carbon pricing lifts headline prices; this benefit accrues primarily to older households (aged 65+), who have large pension income. Other transfers show an increase post-shock but the responses are generally not statistically significant at conventional levels. High-income households show a negative transfer response. Northern and Southern Europe benefit more from the transfer channel, consistent with more generous welfare programs; Eastern Europe shows little or negative transfer response, consistent with weaker automatic stabilizers.

What is the U-shaped pattern of welfare losses by income, and what explains it?

The paper finds that low-income and young households suffer the largest losses (exceeding 1% of three-year consumption), middle-income and older households are most insulated, and high-income households also face significant losses (broadly around the 0.5% average). The U-shape arises from the labor income channel: low-income households are concentrated in sectors and employment types most exposed to carbon pricing contractions; high-income households also have substantial labor income (in absolute terms) that contracts; middle-income households appear more buffered, possibly due to sector composition or greater employment stability. The consumption channel contributes approximately uniformly across income groups (around 0.2%), so does not generate the U-shape.

How does this paper differ methodologically from prior distributional studies of carbon taxes?

Prior work such as Andersson and Atkinson (2020) and Beznoska et al. (2012) focused on direct consumption-price incidence, following Poterba (1989) and using static input-output methods or cross-sectional spending data to estimate first-round price effects. The present paper differs in three ways: (1) it instruments for unexpected carbon price shocks, isolating exogenous variation; (2) it incorporates indirect channels — labor income, asset prices, and transfers — in addition to direct consumption prices; (3) it estimates dynamic IRFs directly, capturing the persistence of effects over a three-year horizon. The key novel finding is that indirect labor income effects are the dominant driver of both the level and the distribution of welfare losses, and that neglecting these indirect channels substantially understates both the size and the regressiveness of carbon pricing.

Why are regional differences in welfare loss so large, and what drives Northern Europe’s relative insulation?

Regional differences are driven primarily by differential pass-through from carbon prices to consumer prices and by differential labor market rigidity. Northern Europe sources a large share of energy from renewables, so a carbon price increase has a smaller pass-through to domestic energy costs. Eastern Europe was allocated disproportionate free ETS allowances over the 1999–2019 sample period, also dampening direct price impacts — consistent with Känzig and Konradt (2024). Southern and Eastern Europe have more rigid labor markets (stronger employment protection, less flexible wage-setting), amplifying the labor-income contraction. Northern and Western Europe have more flexible labor markets. Additionally, Northern and Southern Europe have more generous welfare programs that partially cushion losses via the transfer channel; Eastern Europe lacks this buffer.

What data sources does the paper combine, and what are the key sample restrictions?

The paper combines three Eurostat/ECB household surveys: (1) the Household Budget Survey (HBS), 2015 wave, for consumption basket shares by COICOP categories for demographic groups; (2) EU-SILC (2004 onward for some countries, 2005 for most) for annual labor income and transfer income time series by group, converted to quarterly frequency via Chow-Lin interpolation; (3) HFCS (conducted every 4 years by the ECB) for household portfolio positions. Time-series macro data on HICP components, house prices, bond prices, stock prices, and dividends come from Eurostat and ECB/Bloomberg. The sample covers euro-area countries (excluding Netherlands and Austria for data reasons) over 1999–2019. Households are restricted to ages 25–75; top and bottom 1% by net worth are excluded from portfolio statistics. The base year for all life-cycle variables is 2015.

What are the policy implications and their scope conditions?

Three main implications are drawn: (1) Public resistance to carbon taxes is not merely ideological — the estimated welfare losses are sizable (about 0.5% of three-year consumption for a 1% energy-price increase), so opposition reflects genuine economic concerns. (2) Standard compensation via energy-bill rebates or consumption-basket adjustments is insufficient because the dominant channel is labor income (0.43% vs. 0.19% for consumption). Compensation schemes should include labor-market policies; the authors also suggest expansionary (green) monetary policy as a tool to ease the income burden, though at some inflationary cost. (3) The intergenerational dimension is important: working-age households (especially young, less-educated, lower-income ones) bear the brunt while retirees are largely shielded. Redistribution should run from old to young, not just from rich to poor. Scope conditions: the estimates are derived from the EU ETS context (European carbon market, euro area, 1999–2019), rely on a small-shock linear approximation, and focus on short-to-medium-run impacts (three-year horizon). The benefits of reduced carbon emissions are excluded from the welfare calculation.

How does the paper handle inference given the short time series and estimation uncertainty?

The sample runs from 1999 to 2019, which is relatively short for the IRF exercises. The paper reports 68% and 90% confidence bands throughout (rather than the conventional 95%), using the lag-augmentation approach of Montiel Olea and Plagborg-Møller (2021) to account for serial correlation. For the money-metric welfare calculations, inference uses a parametric bootstrap that draws from the estimated distribution of IRFs (assuming block-wise uncorrelatedness across variables, justified by low cross-residual correlations averaging 0.16). Cross-sectional group shares are treated as given. The authors explicitly acknowledge considerable uncertainty: the 68% confidence band on the aggregate welfare loss spans 0.06% to 0.94%. They conduct joint F-tests for homogeneity of welfare effects across demographic groups; in all cases the null is rejected with p-value = 0.00. Only 68% bands are reported for welfare calculations given short sample and estimation uncertainty.

What are the heterogeneous labor income IRF magnitudes for different groups, and are they statistically significant?

Average labor income falls by about 1% at the peak (imprecisely estimated). Non-college-educated peak fall exceeds 1%; college-educated peak fall is more muted. By income group: low-income households see falls of roughly 2–4% over three years; high-income households see a fall of about 1%; middle-income households fall by approximately 0.5–1%. These effects are noted to be larger than analogous results for oil shocks in the US (Del Canto et al. 2025), attributed to European labor market rigidity. The responses are described as featuring ‘a considerable degree of persistence but only imprecisely estimated’ at the average level. The welfare calculations based on these IRFs have wide confidence bands, reflecting this imprecision.

What are the consumer price dynamics following a carbon policy shock?

Energy prices (HICP energy component) rise by 1% on impact and remain elevated for approximately one year before returning toward baseline. Housing and utilities experience a significant, persistent increase, remaining approximately 0.5% above baseline three years after the shock. Transport prices increase by 0.5% on impact but revert within a year. Food prices rise to a lesser extent. Restaurants and hotels, recreation and culture, and clothing also show significant impact-period increases, though most effects become insignificant after 12 months. Two exceptions at 12 months: housing and utilities remain significantly elevated; education and communication prices actually fall, possibly reflecting adverse general-equilibrium wage and employment effects.

How is the welfare analysis limited to short-to-medium-run effects, and what longer-run effects are left unaddressed?

The welfare calculations are restricted to a three-year horizon because statistical power in the local projections declines beyond that point given the available sample (1999–2019). The paper explicitly notes that the estimates may miss unemployment hazard effects (i.e., transitions into and out of employment), borrowing cost effects induced by carbon taxes, and any long-run structural adjustments (sectoral reallocation, green investment, capital formation). The benefits of reduced carbon emissions — which may be very large in welfare terms but are realized over much longer horizons — are also excluded by design.

Key Concepts

Feasible Set Approach: A welfare-measurement methodology (from Del Canto et al. 2025) that applies the Envelope Theorem to show that the first-order welfare impact of any shock on a household equals the change in the discounted present value of that household’s budget set — encompassing consumption prices, labor income, asset income, and transfers. The measure is preference-free at first order and is expressed in money-metric (income-equivalent) units.

Money-Metric Welfare Loss: In this paper, the number of euros a household would be willing to pay to avoid exposure to the carbon policy shock, computed as a share of total three-year consumption. It is derived from the feasible-set formula and expressed in income units, making it directly interpretable and comparable across demographic groups without requiring preference parameters.

Carbon Policy Shock: An exogenous, unexpected change in carbon prices driven by regulatory events affecting the supply of EU ETS emission allowances, identified via high-frequency shifts in carbon futures prices around those events used as instruments in an external-instruments VAR. Distinguished from demand-driven carbon price fluctuations correlated with the business cycle.

Labor Income Channel: The indirect welfare effect of a carbon price shock that operates through general-equilibrium changes in aggregate wages and employment. It is the dominant welfare channel in the paper (0.43% of three-year consumption on average, versus 0.19% for direct consumption-price effects), and the primary driver of both the aggregate welfare loss and the distributional heterogeneity across education, income, and regional groups.

Consumption Channel (Direct Effect): The welfare impact arising from higher prices for goods in the household’s consumption basket following a carbon price increase. Weighted by the household’s nominal expenditure on each good. Broadly similar across demographic groups (clustering around 0.2% of three-year consumption), so it does not generate the observed distributional heterogeneity — in contrast to the labor income channel.

Portfolio Channel: The welfare effect transmitted through changes in asset prices (equities, bonds, housing) after a carbon shock. The sign depends on whether a household is a net buyer or net seller of the asset: younger households in the accumulation phase gain from falling asset prices; older households in the dis-saving phase lose. Quantitatively small on average (net welfare gain of about 0.04%), most pronounced for younger, college-educated households.

Transfer Channel: The welfare effect operating through government transfer income (unemployment and other social benefits) and inflation-indexed pension payments. Because pensions are indexed to the price level, carbon-induced inflation raises pension income and benefits older households. Other transfer income tends to rise post-shock but the responses are generally imprecisely estimated. On average the channel generates a modest welfare gain (about 0.07% of three-year consumption), primarily for the elderly.

Greenflation: The phenomenon, documented empirically by Bettarelli et al. (2025) and referenced in this paper, whereby carbon-tax shocks contribute to broader consumer price inflation beyond the direct energy-price impact — through pass-through to housing, transport, food, and other categories, and by raising inflation expectations and triggering tighter monetary policy, which in turn depresses bond and house prices.