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IER Study is Wrong on Kerry-Lieberman

by Michael Levi
June 30, 2010

The Institute for Energy Research has published an analytically weak study written by Chamberlain Economics (CE) that distorts the Kerry-Lieberman energy and climate bill and overstates its costs. Expect it to be much-cited in the coming weeks.

I want to focus on one particular thing that the study does, since I expect some others will decide to copy it, since it is important, and since it is wrong.

The study claims that the bill is highly regressive, i.e. that it will hit poor people disproportionately. How does it do this? The bill gives a large amount of money (in the form of free emissions allowances) to local electricity distribution companies (LDCs) and instructs their regulators to ensure that they use that money for the benefit of ratepayers. Since poorer people tend to spend a larger fraction of their income on electricity, that would tend to blunt any regressive elements of cap-and-trade, as the study authors themselves note. The study, however, argues that the LDCs will actually transfer the value of the allowances to their shareholders instead. Since the typical shareholder tends to be richer than the typical person, this makes the bill regressive.

But their argument for why LDCs will transfer the value of the allowances to shareholders is weak. The LDCs are regulated. The study must therefore argue that they will be able to massively game the regulators:

“Lawmakers can specify the statutory or legal incidence of free emission allowances, but they do not control the actual economic incidence. The ultimate beneficiaries of LDC subsidies are not determined by the text of legislation or the intentions of Congress; they are determined by the economic behaviorof profit-maximizing firms operating within an imperfect state and local regulatory regime.”

So far so good. Whether firms can actually game the regulators in this case depends on the details of the circumstances at hand. The authors continue:

“The question of who benefits from LDC subsidies is analytically similar to the tax incidence question of who pays state and local gross receipts taxes. States typically place the statutory incidence of these taxes on businesses, mandating that revenue officials—who represent the regulatory regime enforcing tax provisions—oversee that firms do not in fact forward-shift burdens onto consumers. In practice, economists widely acknowledge that profit-seeking firms routinely forward-shift gross receipts tax burdens, directly contradicting the statutory incidence specified by lawmakers in legislation.”

Right. But those firms aren’t regulated. If a regulator could say “you aren’t allowed to pass on those tax costs to consumers” then we’d be comparing apples with apples. As it is, we aren’t. The authors also point to the European experience, where the value of free allowances was passed on to consumers. Again, there was no regulatory barrier to doing this, so the case is irrelevant.

The authors, to their credit, go beyond this sloppy analogy, and make a more careful argument about Kerry-Lieberman. They go through some basic microeconomics to argue (I’m simplifying a bit here) that since utilities’ costs will rise under cap-and-trade, and since regulators won’t be able to tell exactly how much of any cost increase is due to cap-and-trade, utilities will be able to play the regulators in a way that lets them capture some of the allowance value.

This would be fair if this exercise was about estimating costs. But it isn’t. It is about measuring value. The regulator knows the value of the free allowances: it is equal to the number of allowances given out for free multiplied by the value of the allowances at auction. If the LDCs cannot account for having spent that money on public purposes, the regulator will know. The CE authors try to make this complicated, but it’s actually pretty simple.

I won’t get into all the other problems with the study here. My biggest other pet peeve is how the study calculates negative employment impacts, which is says will be huge (500,000+ jobs lost by 2015). Short version of the critique: you can’t use an input-output table to estimate employment impacts if all you know is the aggregate GDP impact of the bill (which is all the CE authors know); you need to know the impact on output on a sectoral basis. It turns out that the bill hits capital-intensive sectors disproportionately, so its employment impacts are considerably smaller than what the CE folks project. I’ll expand on this if enough people ask for it in the comments.

Post a Comment 4 Comments

  • Posted by Josiah

    Interesting point regarding the capital intensive sectors being disproportionately impacted. What about the secondary impacts of these sectors? Even if the sectors being primarily hit are labor sparse, capital intensive (I believe this is what you are pointing towards) will the effects on indirect or induced jobs be similarly mitigated? Why?

  • Posted by Stuart Staniford


    It looks like you have most of the text doubled up.

    [ML: Yikes. Thanks for the tip. It should be fixed now.]

  • Posted by Andrew
  • Posted by Andrew

    One criticism made by Levy that we did not address in the rebuttal linked to above is the claim that the estimated employment impacts in our study are invalid because more capital-intensive industries will be more heavily affect than labor-intensive industries by climate policy.

    As we make clear in our study, our figures for potential job losses are only order-of-magnitude estimates designed to give a general idea of the size of the employment effects we can expect from a policy that reduces GDP by the amounts predicted by EPA in various years. We don’t model the entire American Power Act bill. Instead, we show about how many jobs can reasonably be expected to disappear if GDP falls by a given amount, holding all else constant.

    In our study, we assume overall GDP reductions will be felt by industries in proportion to the fossil-fuel carbon intensity of their products. Levi is right that if industries are affected in different proportions than what we assumed, the pattern of employment losses — and potentially the overall total job losses — will differ from our estimates. But it’s easy to see that they won’t differ by much. In fact, it turns out our estimates are robust across a wide variety of assumptions about the distribution of GDP impacts among industries.

    To see why, suppose Levi is correct that capital-intensive industries will be most heavily affected by Kerry-Lieberman. Rather than dividing the overall GDP impacts among industries by carbon intensity as we’ve done, we can instead divide it by an estimate of capital intensity by industry. A back-of-the-envelope way of doing this is to use data on the relative share of labor income as a percentage of value added for industries. In capital-intensive industries, labor income will be small relative to total value added. We can then weight these “capital intensity factors” by total industry output to arrive at a reasonable proxy for capital intensity by industry. These figures can then be used to distribute overall GDP impacts to industries, consistent with Levi’s argument above.

    Re-running our model using this method, we find the employment effects of Kerry-Lieberman would be significantly *larger* than our estimates — not smaller as Levi assumes. Here are the figures for total job losses in various years under the assumption that capital-intensive firms are more heavily affected:

    2015 : -653,783
    2020 : -895,924
    2030 : -3,511,055
    2040 : -4,915,477
    2050 : -6,440,970

    Overall, these figures are broadly comparable to our original estimates. However, they are higher by roughly 25 percent. It is simply not the case that our study has over-stated employment effects from the bill as Levi claims. To the contrary, if Levi’s argument above is correct, our estimates may in fact may err on the conservative side. This should not come as a surprise—our estimates of job losses should be considered order-of-magnitude estimates, which are unlikely to vary dramatically to changes in the assumption of how overall GDP declines are distributed among industries.

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