Residential Utility Rates’ Effect on Americans’ Willingness-To-Pay for Carbon-Pricing Policies

CLOSUP Student Working Paper Series Number 24 April 2017

Residential Utility Rates’ Effect on Americans’ Support and Willingness-To-Pay for Carbon-Pricing Policies Jacob Podell, University of Michigan

This paper is available online at http://closup.umich.edu Papers in the CLOSUP Student Working Paper Series are written by students at the University of Michigan.

This paper was submitted as part of the Winter 2017 course Environ 302: Energy and Environmental Policy Research, made possible through funding provided by the University of Michigan Third Century Initiative.

Any opinions, findings, conclusions, or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the view of the Center for Local, State, and Urban Policy or any sponsoring agency

Center for Local, State, and Urban Policy Gerald R. Ford School of Public Policy University of Michigan

Residential Utility Rates’ Effect on Americans’ Support and Willingness-To-Pay for Carbon-Pricing Policies Jacob Podell University of Michigan April 26, 2017

Abstract Climate change mitigation policies are often politically challenging to implement; this is especially true in the U.S. where carbon-pricing mechanisms are a political non-starter across both federal and sub-federal levels of government. While previous work has found that designing the policy so that it is somehow “linked” to energy (e.g., a revenue recycling scheme in the form of rebate checks to ratepayers) does increase the public’s support, less attention has been paid to other ways the public’s interactions with energy affect support for these policies. Using a census of residential electrical utility rates in the U.S. and survey data on Americans’ support and willingness-to-pay for carbon-pricing policies, this article finds that the price one pays for electricity does not affect their overall support for these policies. The findings suggest that the concern that a given mitigation policy will result in higher electricity prices need not be a limiting factor when designing these kinds of policies from a political standpoint.

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Introduction Based on overwhelming scientific consensus, climate change is anthropogenic and will have devastating impacts on both human and ecological systems (IPCC, 2013). Since the Industrial revolution, humans have been adding greenhouse gases (GHG) faster than the environment naturally removes them, which is the significant contributor to climate change. The general consensus is also that these harmful impacts can be reduced, i.e., mitigated, by reducing the rate and amount of GHG emissions.

While accomplishing a meaningful level of mitigation is no small task, economists agree that carbon-pricing mechanisms are both the most effective (in terms of net GHG emission reduction) and economically efficient mitigation tool (IMF, 2016). They are usually proposed as either a carbon tax or a cap-and-trade regime. Despite its acceptance in policy circles, carbonpricing remains a political non-starter in the United States. The federal government has not passed any bill of this sort, most notably when the United States Senate failed to pass the Waxman-Markey bill passed by House of Representatives in 2009 1. Even at the state level, which initially appeared promising with 23 states pledging to implement a cap-and-trade program by 2008, now only has 10 states with any form of that policy (Rabe, 2016).

A decrease in public support can be attributed to part of the decrease in total jurisdictions with any form of this policy and why these kinds of policies have such non-starter qualities overall. Rabe and Borick (2014) found a decrease in support for a diverse set of proenvironmental opinions from 2008 to 2013, including several shifts or near shifts in what the majority opinion was, such as: the belief that one’s state should adopt climate policies if the federal government fails to do so (70% to 50%) and support state-level cap-and-trade programs

1

The American Clean Energy and Security Act passed the House on June 26, 2009 by a vote of 219-212. It was never brought to the floor in the Senate and thus subsequently died at the end of the 111th Congress.

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(55% to 32%). Given the significant impact of public opinion on policy outcomes (Burstein, 2003), understanding which factors created this drop in public support can allow for potential leverage points to be identified.

This paper will look at underexplored factors affecting Americans’ support and willingness-to-pay (WTP) 2 for carbon-pricing policies, specifically, the effects, if any, that one’s electric utility bill has on support/WTP. Drawing from analyses of public opinion of the United States and abroad and from case-studies of successful cap-and-trade programs, this paper will analyze public opinion data from the Fall 2015 iteration of the National Survey on Energy and Environment (NSEE) and utility rates for 2015 within the United States.

Literature Review Research on the public’s opinions regarding climate policies is very robust and can be broadly divided into two categories. First, there is the “front-end” of a policy. If a policy design is held constant, what factors affect a person’s support for said policy; standard demographic factors as well as beliefs/ideologies are often found to have a significant impact. The first 2 subsections will cover this front-end. The “back-end” is how a policy’s design or framing can generate or deter support. This will be covered in the final subsection.

The Front-End: Worldviews, Ideologies, and Beliefs In the broadest sense, people’s worldviews tend to affect their support for climate change policies; an egalitarian worldview is positively correlated with support, whereas a more individualistic one is negatively correlated (Drews & van den Bergh, 2015; McCright et al., 2016). A leftist/liberal ideology is positively correlated with support for a given policy. On that trend, partisan identification with a party on the left (whether it be the Democrats in the U.S., or

2

“Support” is defined as an ordinal variable showing if one strongly, moderately, etc. supports/opposes a given policy. “WTP” is an ordinal variable expressed in USD/year.

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the corresponding party aboard) is strongly correlated with support for these policies (Drews & van den Bergh, 2015; McCright et al., 2016). Overall, whether measured by party or ideology, a nearly similar effect is seen; the political left is more support of climate change mitigation, while the right is more opposed to it (McCright et al., 2016). For WTP, political parties in the U.S. had significant effects, with Democrats being positively correlated with a higher dollar amount and vice versa for Republicans (Kotchen et al., 2013).

Belief that climate change is anthropogenic is also one of the stronger predictors of support or higher WTP for a policy (Drews & van den Bergh, 2015; Kotchen et al., 2013; McCright et al., 2016). For several of the studies cited in this paper, a given variable was compared to a person’s belief in climate change, not necessarily how they feel about a given policy. So, that emphasis is important to note in that believing in climate change can be used as a proxy for support/WTP for a particular policy.

The Front-End: Demographics Several international studies have found that standard demographics affect support/WTP across a diverse range of countries. Li et al. (2016) found that 85% of the Chinese are willing to pay at least 10% more for a carbon policy to be enacted, with education, income, and gender (being male) positively correlated with WTP, and age being negatively correlated. Similar results were found for members of the European Union, and Switzerland (Baranzini & Carattini, 2016; Drews & van den Bergh, 2015).

A similar trend can be seen in the United States for WTP. Americans are willing to pay $79-$89 for climate mitigation policies. Both gender and race are in a quasi-stalemate between a positive effect, a negative effect, or no significant effect at all. Meanwhile, education and income are more often than not positively correlated with WTP (Kotchen et al., 2013).

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Turning toward support, Americans’ religion is also an important factor affecting belief in climate change/support for climate change policies. Non-Christians (generally asked in surveys as anyone neither Catholic or Protestant) tend to believe in climate change at the highest rates while Christians (especially evangelicals) tend to believe in it less (Mills et al., 2015b).

McCright et al.’s meta-analysis (2016) highlights the multitude of studies showing how other demographics affect support; education and income are often positively correlated, while gender, race, and age have more mixed results. A summary of the literature cited so far with which frontend/demographic factors they studied can be seen in Table 1.

Of course, not every single study looked at in these meta-analyses had income as being a significant factor for support/WTP. That being said, the perceived personal economic impact/burden is more frequently negatively correlated with support than actual income (Drews & van den Bergh, 2015). So, interestingly, although not necessarily surprisingly, people’s perceptions of a policy’s impact seem to hold more sway than reality. These perceptions can be used in the framing or design of the policy to alter the support it will get. Indeed, there is a large amount of evidence suggesting these back-end edits of a policy can affect its public support; this evidence is looked at below.

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The Back-End: Framing and Revenue Recycling Several studies, both qualitative and quantitative, stress the importance of framing to generate support for carbon policies. Specifically, revenue recycling schemes (a system where a policy returns money generated by the policy to the public) can be fundamental to frame a policy in a way that generates significantly more support.

Starting with qualitative analysis, in a case study of the Regional Greenhouse Gas Initiative (RGGI), a group of now nine northeastern states in the United States with an interstate cap-and-trade program, Raymond (2016) looks at the importance of framing in RGGI’s formation and durability. He found that the Polluter-Pays-Principle, one of the bedrock ideas in environmental policy since the 1970’s, was an important and persuasive argument used by RGGI’s advocates. Given that the atmosphere was a commons, the norm that the entity that ruins the commons (in this case by releasing GHG) should bear the finical burden. However, looking at failed regimes (e.g., the Western Climate Imitative, the Midwestern Greenhouse Gas Reduction Accord, and Australia’s carbon tax/cap-and-trade hybrid from the mid-2000’s), he found that the Polluter-Pays-Principle was necessary, not sufficient. There needs to be some additional frame focusing on how the public benefits from a firm’s use of the atmosphere, since the firm is technically using the public’s “common” property. A key finding of his was that revenue recycling satisfies what he called the “Public Benefit Frame.” Discussions of how the revenue from the auction of permits was going to be given back to the public (and the fact that is was even going to be recycled) were key to convincing either the states’ legislative or executive branch (different states adopted the agreement differently) to enter RGGI. By and large, quantitative studies back up the results of this case study. Internationally, data from EU member-states shows support is limited if the revenue is simply kept in the

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government’s general budget, increases if the money is used to reduce taxes, and increases even more if it goes to some environmental project (Drews & van den Bergh, 2015). Baranzini and Carattini (2016) found the same results in Switzerland. They also found an additional effect of framing; the same carbon tax scheme got more support when called a “climate contribution” rather than a “carbon tax.”

This requirement to design the policy so it can be framed as a public benefit is seen in the U.S. regarding a carbon tax. Amdur et al. (2014) found that there is strong opposition to a carbon tax (approximately a 2 to 1 margin) when no use of the revenue is specified. A similar level of opposition is seen when the proposed revenue is used for deficit reduction. Support increases (to 56%) when the tax is proposed as revenue-neutral, via rebate checks to the public. Support is highest (at 60%) when the proposal has the tax revenue going to research and development for renewable energy.

Mills et al. (2015a) found similar trends when Americans are asked about the potential revenue use for a cap-and-trade program. Only 22% support exists if the revenue is put to the general treasury, 33% for general infrastructure spending, and 41% for a revenue-neutral tax decrease somewhere else. Support increases though once energy or otherwise “green” uses of the revenue are proposed; 44% for renewable energy rebates and 47% when invested into energy efficiency improvements. 3 Overall, these studies show that, in the words of Mills et al. (2015a), policies with revenue recycling somehow “linked” to energy or the climate get more support. This boost that such linkage provides suggests that people are aware of a connection between climate and energy, and the stronger the link, the more support a policy gets. With all the studies cited about

3

While none of these options received a majority, partially due to the large number of “not sure” responses, the last three designs (revenue-neutral, renewable energy, and energy efficiency) received plurality support.

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policy framing and design, this linkage’s effect has been studied heavily on the back-end of policy. However, there is very little on the “front-end,” that is, how people’s interaction with energy, namely how much they pay and how they perceive their energy bills, affects their base level of support and/or WTP for climate change policy, before any revenue recycling mechanisms are proposed. One study did find that support decreases whenever you frame a carbon tax as increasing energy costs/utility rates (Amdur et al., 2014). This does suggest that the energy linkage can have an impact on the front-end. However, this study only looked at hypothetical changes in energy rates, not how current rates are affecting support. In essence, does one’s utility bill affect their WTP and/or support for a carbon-pricing policy?

Methods Data Sources In fall of 2015 an iteration of the NSEE was conducted by the Center for Local, State, and Urban Policy (CLOSUP) at the University of Michigan and the Muhlenberg College Institute of Public Opinion. This telephone survey worked to capture public opinion with randomly selected phone numbers from across the U.S. of individuals over 18 years old (N=911). Both landline and cell phones were included. The response rate for the survey was 12%, and the results were weighted by age, race, total education, gender, and income to reflect United States Census Bureau parameters for 2013.

Zip codes of the respondents were collected in the NSEE and used to join those data with average utility rates based on zip code for both investor-owned utilities (IOU, i.e., private utilities) and non-IOU’s (i.e., public utilities). This information was compiled by the National Renewable Energy Laboratory (NREL) using sources from the ABB Group (an energy and

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technology consulting firm headquartered in Switzerland) and the U.S. Energy Information Administration. This data was not weighted as it was a census.

Variables The dependent variables are respondents’ WTP for climate change policies (specific to the NSEE, the question is phrased as “If it required you to pay extra money each year in order to reduce greenhouse gas emissions, how much would you be willing to pay?”) and overall support/disapproval of climate mitigation policies (for the NSEE, they ask about cap-and-trade at the state level). The former is an ordinal variable as respondents’ individual WTP are grouped into 6 brackets with non-equal ranges (e.g., $50-100/yr., $100-250/yr., etc.), with the max being “$500/yr. or more.” An increasing number means a willingness to pay more money annually.

The latter is also an ordinal variable organized by level of support (e.g., Strongly support, somewhat support, somewhat oppose, etc.) with an increasing number showing an increase in support. Summary statistics for all variables are presented in Table 2.

It is worth noting the survey question about support is specifically asking about a capand-trade regime. However, Americans do not have a preference between various climate mitigation policies; when the individual costs were held constant, there was little difference of support between a tax, cap-and-trade, or a command-and-control limit on GHG emissions (Kotchen et al., 2013). Even a gas tax, which is generally not studied together with cap-and-trade or a carbon tax, shows similar patterns of support (Kaplowitz & McCright, 2015). Overall, this means that the analysis conducted in this paper does not necessarily need to be limited to the effect of utility rates on support for cap-and-trade specifically.

The residential utility rate paid by the respondent is the primary independent variable of interest. The rates are in USD/kilowatt-hour. These data are a continuous variable. When a given

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zip code had more than one utility that charged different prices, the rates were averaged for said zip code. Zeros were treated as “system missing.”

The model includes a standard set of demographics collected from respondents to the NSEE to serve as control variables: age (ordinal variable of groups between ages of 18-65+), education (ordinal variable from less than high school to graduate degree), income (ordinal variable from less than $20,000 to over $100,000), political ideology (ordinal variable from “very conservative” to “very liberal”), political party (reclassified into 2 binary variables: “Democrat” and “Independent/Other;” Republicans are thus the reference case), gender (reclassified into a binary variable where 0= “not male” and 1= “male”), race/White (reclassified as a binary variable where 0= not white and 1= white), and religion (reclassified into 2 binary variables: “Catholic” and “Protestant;” all other religions are thus the reference case).

The final control variable is outside standard demographic factors. The literature showed that the belief that global warming is anthropogenic has a significant impact increasing support and/or WTP (see Table 1). As such it is included in the model as a reclassified binary variable where 1= a belief that it is “solely human caused” and 0= “not solely human caused.”

To analyze the data, an ordinal regression was conducted for each independent variable.

For all variables in the model, unless otherwise stated, “don’t know/not sure” or “refused” responses were marked as “system missing.”

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Table 2. Variable Summary Statistics

Variable WTP

Type Mean/Mode4 Ord

2.231/1

Std. dev.

Min.

Max.

1.392

1 (Nothing)

6 (>$500/yr.)

1.143

1 (Strongly

4 (Strongly Support)

(Nothing/$0) Level of Support for a

Ord

cap-and-trade program

2.277/1 (Strongly

Oppose)

Oppose) Residential Utility Rate

Cont

.1201561940

.0286415064 .0692701348

.3134605042

Age

Ord

2.56/3 (45-64)

.034

1 (18-34)

4 (≼65)

Education

Ord

3.20/3 (Some

1.121

1 (less than

5

college or

high school

(graduate/professional

technical

graduate)

degree)

1.547

1 (<$20,000)

6 (>$100,000)

1.174

1 (very

5 (very liberal)

(in USD/kWh)

school) Income

Ord

3.40/3 (40,00060,000)

Political Ideology

Ord

2.9/3 (Moderate)

conservative)

Democrat

Bin

.388

.488

0

1

Independent/Other

Bin

.356

.479

0

1

Male

Bin

.487

.500

0

1

4

As applicable based on variable type. For all ordinal and categorical variables, the translation of its mode, minimum, and maximum values are in parentheses.

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White

Bin

.646

.479

0

1

Catholic

Bin

.279

.449

0

1

Protestant

Bin

.389

.488

0

1

Belief in Global

Bin

.410

.492

0

1

Warming

Results WTP After the regression was conducted, WTP came back with 6 significant results (1 more than Support, which is discussed below). The price paid for electricity did not have a significant effect on a personâ&#x20AC;&#x2122;s WTP for a carbon-pricing mechanism. Education and income were both significant and positively correlated with WTP; the more money one made or education one has, the more money they are willing to part with for a climate policy. Income had the lowest p-value of any variable in either model, which the program reported as .000 due to a rounding error (see Table 3). Maleâ&#x20AC;&#x2122;s also had a higher WTP. The pair of binary variables for political party also had significant results. A respondent who identified with either the Democratic Party, an independent party, or a party not listed in the survey were more likely to have a higher WTP. Thus, Republicans (the reference case) are significantly willing to pay less.

A respondent who believed that climate change is anthropogenic is also willing to pay more to prevent it. This had the third highest coefficient behind only by the two political party variables and the third lowest p-values, behind only income and Democrat.

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Using the Nagelkerke pseudo R2, 32.1% of responses can be predicted by this model. All other variables came back with non-significant results. A summary of results is below in Table 3.

Table 3. Results of Ordinal Regression for WTP. Significant results are highlighted.

Variable

Coefficient

Significance

Residential Utility Rate (in USD/kWh)

N/A

.203

Age

N/A

.202

Education

.257

.073

Income

.431

.000

Political Ideology

N/A

.489

Democrat

1.19

.008

Independent/Other

1.02

.022

Male

.653

.033

White

N/A

.587

Catholic

N/A

.809

Protestant

N/A

.905

Belief in Anthropogenic Global Warming

.658

.040

Support Support came back with 5 significant results, only one less than WTP. Like WTP, the residential utility rate was not a significant factor for oneâ&#x20AC;&#x2122;s support. Again, like WTP, income and education were significant; the more education or income one had, the more likely they would support these policies.

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Like WTP, belief in the fact that climate change is human-caused was positively correlated with an increase in support.

The pair of binary variables for religion had a significant and large effect on support.

Both coming back with significant results, Protestant and Catholics had the first and third largest absolute value for their coefficients, respectively, both negatively correlating with support. Thus, the reference case of â&#x20AC;&#x153;other religionsâ&#x20AC;? is more likely to support a cap-and-trade program.

The Nagelkerke pseudo R2 reports that 12.8% of responses can be predicted by this model, which is notably low. All other variables came back with non-significant results. A summary of results is below in Table 4.

Table 4. Results of Ordinal Regression for Support for a Cap-and-Trade policy. Significant results are highlighted.

Variable

Coefficient Significance

Residential Utility Rate (in USD/kWh)

N/A

.232

Age

N/A

.974

Education

.261

.029

Income

.167

.056

Political Ideology

N/A

.488

Democrat

N/A

.816

Independent/Other

N/A

.508

Male

N/A

.378

White

N/A

.926

Catholic

-.528

.098

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Protestant

-.731

.014

Belief in Anthropogenic Global Warming

.555

.032

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Analysis Utility rates, the independent variable of interest, did not have significant results for either of the dependent variables. A likely reason of this is that people simply are not aware of how much they pay for electricity. So, they cannot consciously or unconsciously have it affect their opinions. This makes sense given that income, which people are quite more aware of, continuously shows up as a significant factor.

This point is furthered by Amdur et al. (2014). People have a certain level of support for a carbon tax, which drops when one says that that tax will raise energy costs. So, higher energy costs make people less likely to support this tax. If people were aware of their utility rates, one would expect to see the rates negatively correlate with support/WTP. Since that negative correlation is not present though, it further suggests that people are simply unaware of their utility rate because it does not seem to be affecting support/WTP. The only 3 variables to show up as significant for both dependent variables were education, income, and belief in anthropogenic climate change. This is consistent with the literature which by and large shows that these factors are very likely to be significant, positively correlating with support/WTP (see Table 1).

In the literature review, studies were less consistent on the significance and the direction of that significance for age, race, and gender (see Table 1). So, it is not surprising that only gender came back as a significant result for WTP, while the other 5 results were all insignificant.

This analysis adds to the general lack of uniformity in the literature for these factors.

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Very little of the literature has researched religion’s effect on policy. Instead, there is a strong negative correlation between identification as a Christian and belief in anthropogenic climate change (Mills et al., 2015b). As stated earlier, belief can be used as a proxy for policy support (Drews & van den Bergh, 2015; McCright et al., 2016). However, it is not a perfect metric. This might explain why this variable came back as significant for one dependent variable and not the other.

This paper’s results diverge most from the literature around political parties and ideology, which says that both are strong factors in statistical models and show similar trends whether measured by ideology or party (see Table 1). In this paper’s model, political parties only came back as significant for WTP, and ideology was significant for neither. For support, the model has a rather low R2, which might explain why both parties and ideology have uncharacteristically high p-values. For WTP, the fact that political parties came back as significant and ideology did not might be explained by McCright et al.’s (2016) findings that parties are statistically significant slightly more often that ideological beliefs. Although, it is worth highlighting that this difference between party and ideology is small.

Conclusion This paper suggests that residential utility rates for electricity do not have a statistically significant effect on one’s WTP or support for climate change policies. It does have some limitations though. Most models do not look at both political party and ideological identification, and this might have played a part in this paper’s results for those variables diverging from the literature reviewed. Future research could work fully understand their individual effects.

Continuing with the impact of politics, the NSEE for Fall 2015 asked about support/WTP for climate change policies in the context of President Obama’s then-existing Clean Power Plan

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(CPP). Framing it in the context of the CPP, a regulation promulgated from a highly polarizing president, may have elicited a different response than oneâ&#x20AC;&#x2122;s general/abstract support of this policy. Next, climate change is a hotly contested issue in American politics, and opinions change quite quickly. While the survey data from 2015 is only 2 years old, (and was used since 2015 was the most recent utility rates data available) opinions could have changed rapidly and would not be reflected in the responses used for the models. Lastly, and arguably most significantly, the imprecise way of linking respondents to energy prices limited this study. Utility rates came from one source, and all other public polling data came from the NSEE, which were joined by zip code. While this did allow for a useful approximation, it could not get the exact amount of money a particular respondent paid per month for electricity. This means that unique factors such as a special deal with the utilities company or even the gross total of a monthly bill (regardless of the proportional rate) were simply not included in the model.

Besides future surveys directly collecting a respondentâ&#x20AC;&#x2122;s utility rate to alleviate the problem identified above, future research could explore different aspects of residential energy usage on WTP/support for climate change mitigation. A question this survey could not answer was if people are even aware of how much they pay for electricity. Answering that in future research could go a long way to understanding how electricity rates affect support/WTP. Additional research could also look into other aspects of the publicâ&#x20AC;&#x2122;s perceptions of energy consumption. For example, Americans are incredibly aware of gas prices and their fluctuation. A similar zip code level analysis might be conducted with gas prices.

Regardless of if people are aware of their rates or not, the literature suggests that perceptions of energy costs can have larger impacts than actual energy costs (Drews & van den Bergh, 2015). Research into if people feel they are paying too much/too little/the right amount

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for electricity or recent changes in their rates could yield interesting results on how it impacts support/WTP. As mentioned earlier, this paper only measured how base rates for energy, not gross energy usage, affect support/WTP, thus suggesting another area for future work. Overall, the literature shows that certain interactions with energy does affect support/WTP for climate change mitigation policies and further understanding what perceptions of energy are impactful to the general public and exactly how those interactions affect support/WTP are still ripe areas for policy research.

Despite the general international consensus that climate change is a serious threat that can be reduced with policy responses, carbon-pricing mechanism are a political non-starter across all levels of government in the U.S. Even getting past the science denialism, there is still concern among policymakers that any climate change policy will encounter massive unpopularity. An often-cited concern is that any bill like this would trigger unjustifiable hikes in energy bills for Americans, thus leading to backlash. In fact, this was in part how lobbyists defeated the Waxman-Markey bill in 2009. Ultimately, fear of increasing their constituentsâ&#x20AC;&#x2122; energy bills was a significant factor in the death of that bill (Weiss, 2010). However, this paper suggests that for a policymaker voting or designing a carbon-pricing policy, the concern that electricity prices will go up need not be a limiting factor. While there are certainly factors that greatly affect support for these kinds of policies, often creating visceral opposition, policymakers are well advised to carefully differentiate between which factors might have a negative, positive, or even no impact at all on support for government policies to mitigate anthropogenic climate change.

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