Designing climate policies that do not disproportionately burden vulnerable groups is critical to ensuring public support, and hence their successful implementation. Despite climate justice advocates continuing to highlight climate inequities along racial, gender, and class dimensions and policymakers’ vague statements in support of a ‘just transition’, there are few concrete plans.1 For example, the think-tank E3G recently called for a strengthening of the social dimension of the Fit to 55 proposal, the EU’s flagship climate, energy, transport, and industry policy package (E3G 2021). A notable example is the response of French citizens in 2018 to the planned increase of the federal carbon tax to €86.2 per tonne of CO2e, which fuelled large-scale opposition against what was perceived to be an ineffective additional tax that would reduce the purchasing power of low-income and rural households (e.g. Douenne and Fabre 2020). No wonder politicians caved in.
Smart recycling of revenues from carbon pricing holds the potential to offset distributional concerns and increase the political feasibility of these instruments. How to best recycle revenues is still subject of debate, as different recycling mechanisms have different implications for equity and efficiency, with heterogeneity in impacts across different groups. Moreover, many economic models do not accurately account for the behavioural response of households to price changes, nor for endogenous responses of the economy, such as labour supply, to changes in carbon prices and the way in which revenue is recycled. To shed light on this matter, we study the scope for revenue recycling in the UK, following a recent study on this topic for Germany by van der Ploeg et al. (2020).
Vertical and horizontal inequities
There is vast empirical evidence of inequities in both climate vulnerabilities and the energy burden. The distributional implications of carbon pricing vary across income groups (vertical) and within income groups (horizontal). The latter are fuelled by differences in energy consumption and carbon intensity due to factors such as family size, geography, dwelling infrastructure, electricity-generating infrastructure, commuting patterns, and energy efficiency of durable goods. Horizontal inequities depend on whether the dwelling is insulated or not, on the degree of access to clean energy, and on how far people live from work. Indeed, carbon pricing is generally found to be regressive, as the incidence of taxes on fuels and electricity falls proportionally more on poor households, with mixed evidence for transport costs (e.g. Poterba 1991, Metcalf 1999, West and Williams 2004, Williams et al. 2015). Horizontal inequities are often larger than vertical inequities (e.g. Pizer and Sexton 2019), so that compensating mechanisms that condition only on income may not dampen the heterogeneity in tax burden. For example, Douenne (2020) finds that rural residents have a relatively high demand for transport but less access to public transport, inhabit larger dwellings, and are more exposed to weather fluctuations, which results in higher demand for heating. A growing body of work highlights racial disparities in the energy burden. For example, Reames (2016) and Bednar et al. (2017) find that the energy burden is larger in high-minority than low-minority neighbourhoods in the US. Horizontal inequities are important but are difficult to address via transfers as any compensation which conditions on factors that determine energy use mutes price effects and offsets the ability to abate emissions.
Disaggregated demand for consumption and labour supply
From UK microdata, we estimate the distributional implications of a relatively high carbon tax (£100/tCO2e) on households of different income and composition (e.g. Paoli 2021). To account for the response of households to increases in the cost of items that – directly or indirectly – rely on taxed carbon equivalents, we estimate the income and price elasticities of demand with respect to seven aggregates of goods. We estimate a very flexible demand system (the so-called EASI demand system) where budget shares depend nonlinearly on net income of households.
Figure 1 Household budget shares vary nonlinearly with total expenditure
These nonlinear relationships are displayed in Figure 1, which indicates that the budget shares of durables increase with income, while budget shares of food, heating, and electricity decrease with income. The budget share for transport first increases and then decreases with income.
We allow for heterogeneity in income and household composition, but we also report group-specific price and income elasticities. Our empirical findings indicate that food, heating, and services are necessities, which is a source of carbon tax regressivity. On the other hand, transport, housing, and durables are luxury goods. The heating elasticity is highest for low-income households and falls with income (except for the top income quintile); these larger responses may push low-income households into energy poverty. Substitution effects are stronger for food and energy (heating and electricity) among lower-income households, while we find similar elasticities across income quintiles for the remaining product categories.
We complement our estimated demand system with estimates of labour supply, which gives hours worked as an increasing function of the real after-tax wage. Our estimated labour elasticities differ per household, but the average implies that a 10% increase in the after-tax wage boosts labour supply by 33%. Hence, carbon taxes push up consumer prices and depress the real wage and labour supply. If some of the revenue is used to cut income taxes, this mitigates (or possibly even reverses) the drop in labour supply.
Different modes of recycling carbon tax revenues
We consider five ways for the government to use the carbon tax revenues: (A) no recycling, or using the revenue to finance useless government spending; (B) increase social security benefits proportionally, capturing an expansion of the safety net; (C) hand out lump-sum rebates to households, the same rebate for each household, adjusted by family size; (D) lump-sum rebates, adjusted for family size and targeted to lower income quintiles; or (E) lower income tax rates. Our analysis allows us to compute so-called equivalent variations (EVs), of policy changes, which correspond to how much households are willing to pay to avoid any of these five carbon tax policy packages. We calculate these for each household under each of the packages. Computing this for each household and differentiating by family composition and income level depicts the landscape of winners and losers from each policy package, whereby account is taken not only of the tax hike but also of the endogenous responses of the economy. By calculating the number of households who find an improvement of their utility, we obtain crucial insights on the political feasibility of each policy package.
Effects of a carbon tax when revenues are not recycled (package A)
Figure 2 presents the effects on equivalent variations expressed as a fraction of household income when carbon tax revenue is not recycled (package A). We confirm that carbon pricing is regressive with carbon taxes representing almost 8% of weekly expenditures for the lowest income decile and around 5% for the richest households.
To investigate the determinants of households’ willingness to pay, we regress the EVs on many demographic, geographic, and urban characteristics. Tax incidence has been found to be higher for rural households in some contexts, for example in France by Douenne (2020), but not others, for example in Germany by van der Ploeg et al. (2020). While rural households experience higher welfare costs in the UK, we show that these are driven by income dimensions and transport costs (while highlighting that we lack accurate data on dwelling vintage and energy efficiency). Single households, those with consumption below the expenditure poverty line, households who live in houses instead of flats, and those with high motoring costs experience higher welfare costs of a carbon tax when revenues are not recycled. Those who spend more on public transport than on motor fuels are clearly less hurt by the policy, which highlights the potential mitigating role of access to public transport.
Figure 2 Welfare effects if carbon tax revenue is not recycled (package A)
Effects of a carbon tax when revenues are recycled (packages B, C, D, and E)
Now consider recycling via transfers and scale up of the social safety net. Carbon tax revenues fund a 34% increase in social security payments, which include social security for retirement and other purposes, including welfare transfers (B). Alternatively, they fund an average of £32 weekly (£1,664 annual) in lump-sum rebates to all households (C), an average of £84 weekly (£4,368 annual) in rebates to the bottom 40% of the equivalised income distribution (D) or an across-the-board income tax reduction of 18%, where the fall is proportional for each tax bracket (E). Emissions fall by less than with no recycling, between 9.5% and 11%, since consumption increases.
To compare aggregate outcomes of recycling mechanisms, Table 1 illustrates the effects on emissions, hours worked, consumption, the percentage of households who benefit from the package (the percentage of households with positive EV), and the changes in Gini coefficients (to capture inequality). Targeted transfers (D) lead to the largest fall in vertical inequality, while using revenue to decrease the income tax rates (E) leads to an increase of inequality. Recycling via social security benefits (B) provides a middle ground, benefitting households across the income distribution in a progressive manner.
Table 1 Aggregate effects of the five policy packages
Figure 3 plots the welfare effects for the various policy packages with recycling as a share of income for each income decile and household type (to capture heterogeneity across household groups), showing 25th, 75th, and 75th quantiles. Recycling revenues via per-capita transfers (C, D) or by expanding social security benefits (B) renders the policy progressive (a positive EV/income reflects a welfare gain): lower income deciles experience welfare gains especially when revenues are recycled via targeted transfers. Higher income deciles, however, experience welfare costs, albeit small. In contrast, when revenues are used to lower the income tax rates (E), higher income deciles gain more than lower deciles (although with large within-income heterogeneity). Expanding the social safety net (B) results in the largest increases in consumption, as the pre-existing structure of social security payments targets households with high propensity to consume, as well as the largest overall percentage of households with net gains.
Figure 3 Effects on welfare of recycling carbon tax revenue
(B) Increase in social security
(C) Per-capita transfers
(D) Per-capita transfers, targeting bottom 40%
(E) Across-the-board income tax rates cut
These results highlight not only the potential for the social safety net, but also that recycling does not ensure a majority of winners. Previous estimates from Germany suggest otherwise. Van der Ploeg et al. (2020) find that recycling all revenue from a carbon tax via lowering income taxes is efficient in that it not only boosts employment and consumer spending, but also results in the majority of households being better off, albeit hurting low-income households. They then propose recycling part of the revenues as lump-sum transfers to compensate the lower incomes; as long as this revenue share does not exceed 60% and the rest is used to lower the income tax, the package is still politically feasible.
Offsetting the distributional effects of pricing is of paramount importance to reach the high carbon taxes required to fully account for the social cost of carbon. Since having a minority of large winners but a majority of small losers incites public antagonism towards a policy, it is crucial to study disaggregated measures of costs and benefits at the household level. Our main conclusion is that rebating carbon tax revenues through social security payments renders the policy progressive and benefits the highest share of households in our sample, at 35%. When evaluating policy packages, accounting for endogenous responses of the economy and heterogeneous effects of recycling revenues helps to identity policies which effectively address social dimensions.
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1 Moreover, existing plans are often narrow in scope. For example, transition strategies of the Just Transition Fund are only directed towards regions directly dependent on fossil fuel industries.