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Distributional impacts of carbon pricing: A general equilibrium approach with micro data for households

Many policy proposals to limit greenhouse-gas emissions revolve around efforts to tax carbon emissions. But many studies point out that such energy taxes are regressive. This column models the distributional impacts of carbon pricing on over 15,000 US households, challenging the view that the policy by itself is regressive.

The distributional impacts of energy and climate policies can be assessed across a number of dimensions. Goulder and Parry (2008) note that two dimensions in particular have attracted attention:

  • the impact on energy-intensive industry; and
  • the impact across households of differing incomes.

The latter dimension plays a particularly significant role in policy circles given the results from a large number of studies indicating that energy taxes – and by extension carbon pricing policies – are regressive.

Studies that have documented the regressivity of energy taxes include Bull et al. (1994), Metcalf (1999), Dinan and Rogers (2002), West and Williams (2004), and Bento et al. (2009) among others. These various studies make an important set of points, two of which stand out.

  • First, how energy tax revenue is used affects the ultimate incidence of the green tax reform.

The incidence of an environmental or energy tax reform can differ significantly from that of the tax considered in isolation. The use of the revenue can undo any regressivity in the environmental or energy tax through a progressive use of funds.

  • Second, regressivity impacts are sensitive to assumptions about whether households are ranked over an annual or lifetime income measure.

Lifetime income measures reduce the regressivity of energy tax and carbon pricing policies as shown by Poterba (1989, 1991), Hassett et al. (2009), and others.

In previous work, we have used a new simulation model of the US economy to explore distributional implications of various ways of distributing allowances from a cap-and-trade system (Rausch et al. 2010a) and alternative schemes for returning revenues from an auctioned cap-and-trade system or equivalently a carbon tax (Rausch et al. 2010b). In a recent study, we have extended the model to endogenously incorporate 15,588 households from the US Consumer and Expenditure Survey within a general equilibrium framework (Rausch et al. 2011). This allows us to explore the distributional impacts of carbon policy over a number of new dimensions that have previously not been explored. 

Recent research using micro datasets

We consider a climate policy with an equilibrium carbon price of $20 per ton carbon dioxide-equivalent and distinguish three revenue distribution schemes.

  • In the first scenario, labelled inctax, revenue from auctioning of permits is used to lower marginal income tax rates to provide efficiency benefits as discussed in a large literature on the Double Dividend (see Goulder 1995 for a description of the literature).
  • The second scenario (percapita) distributes the revenue to households on an equal per-capita basis.
  • A third scenario (capital) allocates the revenue to households in proportion their capital income approximating the free allocation of permits.

Variation in impacts from carbon pricing arises for three reasons. First, households differ in how they spend their income. Carbon pricing will raise the price of carbon-intensive commodities and disproportionately impact those households who spend big on these commodities. In a general equilibrium setting, carbon pricing also impacts factor prices. Households that rely heavily on income from factors whose factor prices fall will be adversely impacted. In the public finance literature on tax incidence, the first impact is referred to as a “uses-of-income impact” while the latter a “sources-of-income impact” (see, for example, Atkinson and Stiglitz 1980 for a discussion of incidence impacts). Third, regional differences in the composition of energy sources affect the carbon content of various commodities, most notably electricity.

Our analysis shows a number of results.

  • First, how proceeds of a carbon pricing policy are used affects both the efficiency and equity of the policy. Using revenues to cut tax rates has beneficial efficiency consequences but can come at the cost of higher regressivity (see Figure 1). Such is the case when comparing a reduction in income tax rates to a uniform lump-sum distribution of revenues. On the other hand, certain distributions have adverse consequences on both efficiency and equity. On these grounds, and abstracting from political economy motives, we cannot find an easy justification for the free distribution of allowances in a cap-and-trade system to industry.

Figure 1. Average welfare impacts by income group

  • Second, previous policy analyses have been carried out using models with a single representative agent or a small number of households. This analysis uses a model with a large number of households and therefore provides finer level detail on distributional impacts of various policies.

Figure 2 illustrates the point that variation in impacts within broad socioeconomic groups may swamp average variation across groups. For example, if the carbon revenue is recycled through marginal income tax rates we find that over one quarter of households in each income decile benefits while the largest negative burdens within 1.5 of the inter-quartile range are about 1% of annual income.

Figure 2. Box plots by income decile

  • Third, we provide two measures to proxy for lifetime income to address the criticism that studies using annual income bias carbon pricing towards greater regressivity. Using a proxy that restricts attention to households where the head of the household is in the prime working age and a measure that classifies households according to educational outcome, we do not find evidence of such bias in our analysis.
  • Fourth, we note that source-side impacts of carbon pricing have typically been ignored in the literature. Doing so biases distributional studies towards finding carbon pricing to be regressive. We find that progressivity on the source side is sufficiently strong to offset regressivity on the use side so that carbon pricing is proportional to modestly progressive.

We trace our result to the dominance of the source-side over the use-side impacts of the policy. It stands in sharp contrast to previous work that has focused only on usage, and has hence found energy taxation to be regressive. The treatment of transfers is also important in driving this result. Lower-income households derive a large fraction of income from government transfers and, reflecting the reality that over 90% of transfers in the US are explicitly indexed (Fullerton et al. 2011), we hold the transfers constant in real terms. As a result this source of income is unaffected by carbon pricing, while wage and capital income is affected.

Figure 3 shows the roles that source- and use-side heterogeneity play in driving the burden impacts across households. The blue line shows the actual carbon pricing burden when we ignore the distribution of allowances and allowance value. The red line shows a counterfactual distribution where we assume all households have the same expenditure shares on different consumption goods regardless of income. Any differences in burden then are driven by differences in sources of income across income groups. The green line shows a counterfactual distribution where we assume all households have the same factor income shares regardless of income. Any differences in burden then are driven by differences in uses of income across income groups. Use-side impacts (green line) show the regressive result found in previous analyses. Sources-side impacts, in contrast, are progressive.

Figure 3. Relative sources-side vs. uses-side impacts across income distribution

Finally we note that advances in computing power and numerical techniques make solving numerical general equilibrium models with large numbers of households quite tractable. This analysis contributes to a growing literature on the impacts of climate policy on households, and it provides a brief look at the possibilities for understanding differential impacts of policies across different socioeconomic dimensions. The general equilibrium analysis improves on previous analyses that focus on uses side impacts only. Differential impacts on income will be important and this study should help guide researchers in thinking about burden impacts more fully in future work.


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