Economy

Yikes.

*Ohio (Come Back to Texas)

I’m doing a small study at Fire Mountain Trails (FMT) in Cherokee, NC: 

The Fire Mountain Trails are Cherokee’s source for big adventure—a multiuse trail system that’s made to mountain bike, hike, or run. The network of trails is more than 11 miles total, so there’s plenty of room for everyone to recreate safely, responsibly…and flowy?

That’s right—if you like your trails with a nice flow of features, with fun berms and quick hits of elevation that are manageable and fun, Fire Mountain is made for you. You’ll find tables, rock gardens, and blinds for those who know, along with single-track and wider sections, spots that are smooth and fast, and trails that invite the more technically accomplished with options for those less so. The trailhead is located at 160 Indian Village Road, about 100 yards from the Oconaluftee Indian Village in Cherokee and shares a parking lot. The trails interlace through the nearby Great Smoky Mountains, so you already know the views and terrain will take your breath away, even if your recreation of choice doesn’t!

The data collection is going to cost less than $250 out of pocket. Right before Memorial Day weekend we put up a sign with a QR code that takes you the online survey. As of the July 4 week we have a disappointing n=23 responses. We have n=96 responses from outreach on social media. The folks at FMT are interested in any economic impacts generated by the trails. I’m interested in collecting data for students and wondering how the two survey modes differ. Real quick, here is the t-test on distance traveled.

Sample 1 is from the QR code and sample 2 is social media. Social media seems to be missing people who travel farther to get there. I’ll be posting the link on the WNC mountain bike trails facebook next week so, in addition to more responses from the QR code, there should be more responses by the end of summer. 

A couple of years ago I did a similar study at Beech Mountain trails but Beech Mountain spent about $600 to pay App State students to hang out and pass out rack cards with the QR code. That generated about n=400 responses. I’m concluding that a passive QR code is no substitute for college students pleading with potential respondents at the trailhead. 

Here is a view from my hike on one of the trails:

American Economic Review Vol. 113, Issue 7 — July 2023:

The authors mention stated preference research in environmental economics (where it all began, right?) a couple of times. First, we get the obligatory CVM critics pat-on-the-back on page 2010:

The advantages of the stated-preference approach come at potential costs, in particular the concern that actual job choices may differ from stated preferences for jobs (Diamond and Hausman 1994; Manski 1999; Hausman 2012).

They could have chosen some better references to the issue of hypothetical bias, but no. See the comment on Hausman 2012 here. 

But, then the second is more positive (page 2011):

More generally, the stated-preference approach has provided valuable evidence for many economic topics, including environmental policy (Carlsson and Martinsson
2001; Carson 2012), consumer preferences (Revelt and Train 1998), labor supply (Kimball and Shapiro 2008), retirement decisions (van Soest and Vonkova 2014),
and long-term care (Ameriks et al. 2015). 

Congrats to Carlsson, Martinsson and Carson for being singled out from the thousands of articles to choose from!

In spite of some warts (e.g, Hausman 2012), this is progress.

References:

Carlsson, Fredrik, and Peter Martinsson. 2001. “Do Hypothetical and Actual Marginal Willingness to Pay Differ in Choice Experiments? Application to the Valuation of the Environment.” Journal of Environmental Economics and Management 41 (2): 179–92.

Carson, Richard T. 2012. “Contingent Valuation: A Practical Alternative When Prices Aren’t Available.” Journal of Economic Perspectives 26 (4): 27–42.

From the inbox:

Join us online July 11 & 12, 2023, 1:00 p.m. ET to 3:30 p.m. ET for Valuing Nonmarket Benefits. Presenters Vic Adamowicz, Cathy Kling, Nic Kuminoff, Dan Phaneuf, Christian Vossler, and John Whitehead will provide a primer of the field including both the theoretical basis and current state of best practice for the most common methods. Core topics include basic welfare theory, stated preference methods, hedonic property value approaches, recreation demand modeling, hedonic wage studies, benefit transfer, the value of risk reduction, and other special topics. 

Description: The development of methods to estimate welfare changes for use in benefit-cost analysis began as a nascent field in the U.S. in the middle of the 20th century to support decision making for environmental investments. It then progressed as part of regulatory impact analyses required under multiple presidential executive orders beginning with EO 12291 issued by the Reagan administration. At the same time, the valuation of ecosystem services and natural capital throughout the European community expanded quickly with the U.K. Treasury “Green Book” and E.U. Water Framework Directive. Methodological improvements followed rapidly for both stated and revealed preference methods, leading to the now relatively mature set of methods available to practitioners.

These methods are being routinely applied to support benefit-cost analysis of regulatory programs, prioritization of conservation spending in government and non-governmental organizations, natural resource damage assessment, and private firm’s analyses of sustainability issues in their sector.

In this workshop, we provide a primer of the field including both the theoretical basis and current state of best practice for the most common methods. Core topics include basic welfare theory, stated preference methods, hedonic property value approaches, recreation demand modeling, hedonic wage studies, benefit transfer, the value of risk reduction, and other special topics. The workshop is intended for professionals and analysts who use the results of these studies in their work and wish to better understand the basics of these methods. The course should also serve as a refresher for those who have been away from the field and applications for some time and are looking for a brief review of the current state of the art.

Explore fees and register now

I’m covering benefit transfer. 

Current air quality. Oof.

If there’s been one consistent thread since the beginning of Env-Econ.net, it’s our endearing commitment to helping you understand the incentives of gas taxes vs. mileage taxes. 

Well, the debate is back in the news again as the governments debate ways to overcome…  

…the myriad hurdles U.S. states face as they experiment with road usage charging programs aimed at one day replacing motor fuel taxes, which are generating less each year, in part due to fuel efficiency and the rise of electric cars.

So here’s a[n updated] view from the wayback machine at some of issues that arise from a mileage tax:

It’s been [over 16] years now (May 8, 2007) and people still aren’t listening to me. 

Taxing miles creates perverse incentives for fuel efficiency.  A $0.015/mile tax (the size of the tax mentioned in the article) is the equivalent of a $0.015 * X tax per gallon where X is mpg.  In words, a mileage tax increases the tax per gallon the more fuel efficient the car.  Now granted, with higher mpg you use fewer gallons to drive an equivalent number of miles, and in the end, everyone driving 100 miles will pay the same tax.  And from a revenue perspective, that might be OK.  But there might be a way to kill fewer birds with one stone.

As I have written a number of times, a more straightforward proposal is to simply raise the gas tax.  Reaising the gas tax accomplishes a number of things 1) It raises revenue, 2) It discourages miles driven, and 3) It increases the incentive for higher fuel efficiency. 

Because my previous posts on this have been written with an ironic twist (I propose a mileage tax that is inversely proportional to fuel efficiency and then show that such a tax is the equivalent of a $1/gallon gas tax), here’s the direct, non-ironic version:  A $1/gallon gas tax…

…places a higher burden on those driving less fuel efficient vehicles–that should satisfy those blaming the SUV drivers for all of the problems*.

…places a higher burden on those driving more.  By increasing the marginal cost per mile driven, total miles driven should decrease.

…assuming fuel efficiency and income are negatively correlated–that is, the rich tend to drive larger, more expensive, less fuel efficient cars–[higher gas taxes] place a higher burden on higher incomes.

…provides an incentive for drivers to switch to more fuel efficient vehicles.

It’s really simple.  Why worry about complicated milage programs?  The gas tax infrastructure is in place.  Raise the gas tax and meet multiple public policy and economic goals simultaneously.

From the WSJ (Oil Demand Expected to Peak This Decade as EVs Boom): 

Rising demand for crude oil is set to slow to a trickle within five years and peak before the end of the decade, as electric-vehicle uptake surges and developed nations rapidly transition to cleaner sources of energy, according to a prominent energy forecaster.

The International Energy Agency, a group funded by some of the world’s largest oil consumers, expects demand for transport fuels derived from oil such as gasoline will be the first to peak before starting a steady decline—hastened by a sharp uptick in EVs and a long-lasting shift to remote working spurred on by the Covid-19 pandemic.

Rapidly growing Asian economies will continue to prop up the global appetite for oil in the coming years, and demand for jet fuel, naphtha and other oil products with industrial uses will continue to tick higher, the IEA said in a report released Wednesday. But even in China, which has long been the powerhouse of global oil demand, the appetite for crude will slow markedly before the end of the decade. India will surpass China as the main driver of oil growth as soon as 2027, the IEA said.

The forecast, which the IEA made in an annual report that considers oil demand as far away as 2028, isn’t the first time the Paris-based group has laid out a timeline predicting a zenith for oil. But it envisages a far more rapid shift away from fossil fuels than previously expected—a shift that has been sharply accelerated by the Covid-19 pandemic and the energy crisis that followed Russia’s invasion of Ukraine.

Joe Manchin: “If I can’t go home and explain it to the people West Virginia, I can’t vote for it. I just can’t. I’ve tried everything humanly possible. I can’t get there,”

Krugman (Working From Home and Realizing What Matters):

First things first: The reduction in commuting time is a seriously big deal. Before the pandemic, the average American adult spent about 0.28 hours per day, or more than 100 hours a year, on work-related travel. (Since not all adults are employed, the number for workers was considerably higher.) By 2021, that number had fallen by about a quarter.

Putting a dollar value on the benefits from reduced commuting is tricky. You can’t simply multiply the time saved by average wages, because people probably don’t view time spent on the road (yes, most people drive to work) as fully lost. On the other hand, there are many other expenses, from fuel to wear and tear to psychological strain, associated with commuting. On the third hand, the option of remote or hybrid work tends to be available mainly to highly educated workers with above-average wages and hence a high value associated with their time.

But it’s not hard to make the case that the overall benefits from not commuting every day are equivalent to a gain in national income of at least one and maybe several percentage points.

If median household income is $70,000 and 1 earner in each household works full-time then the household wage is $35. If time is valued at 1/3 of the wage (the number typically used in travel cost demanad models) then the average household enjoys $1200 in additional time at home (100 hours at $12). If there are 125 million households in the U.S. then the number aggregates to $150 billion.

That is lower, 0.65%, Krugman’s 1-3% of US GDP ($23 trillion) estimate. There is some slippage between households and individual adults here, but you get the idea. Krugman is making assumptions less conservative than mine. 

I participated in a survey conducted by Clemson this week. I was eligible because I had published a paper using opt-in panel data at some point. I posted the image to the right to twitter and proceeded to provide a brief review of what I thought about each of the panels I’ve used. I’ve been thinking about that and want to say more. During the rare times I’ve had enough money in the research budget I’ve used KN/GfK/Ipsos‘s Knowledge Panel (KP). KP is a probability-based sample and more representative of the population than opt-in panels. Opt-in panels are basically convenience samples. There are interesting research questions about if and when researchers should use opt-in panels. A forthcoming Applied Economics Policy and Perspectives symposium is a step in that direction (here is the second, I think, of four articles to appear online). 

The first time that I enjoyed a probability-sample was when I was working on Florida’s BP/Deepwater Horizon damage assesssement with Tim and others. We had plenty of funding for two (!) KP surveys and two articles have been published (one and two [the first, I think, of the AEPP articles to appear online). The second time was a few years ago with funding from the state of North Carolina where Ash Morgan and I looked at the invasive species Hemlock Wooly Adelgid (HWA) and western North Carolina forests. I’ve presented papers from that study at a couple of conferences and UNC – Asheville but nothing publishable. I hope to write the forest paper this summer because it boasts the same coincidental design as the second published paper above. GfK supplemented the KP sample with opt-in responses (while charging us the same price per unit) so there is a data quality comparison between probability-based and opt-in samples. In the second published AEPP paper with a single binary choice question we find that the opt-in data was lower quality. In the HWA study we aren’t finding many differences. In other words, the opt-in data is as good as the probability-based data.  

I think that these opt-in panels will be increasingly used in the future and we need to figure out how best to use them. Opt-in data are much less expensive. For example, a Dynata recreational user respondent cost me $5 in a February 2023 survey. A KP recreational user cost $35 per unit. Of course, KN/GfK programmed the survey while I program my own when using the Dynata panel but programming yourself doesn’t cost much more when you are writing the questions and trying to explain how to do it to KN/GfK. One known problem with opt-in panels is that you don’t get a response rate but it is a toss up whether no response rate is worse than a response rate of less than 10% from a mail survey. The good thing about a mail survey is that you know what sort of bias your data will suffer from (sample selection). I don’t have an estimate of the cost of a mail survey but it is much higher than $3.50 when the response rate is less than 10%. 

I attended this workshop where four of us provided comments on five stated preference studies funded by the EPA that have been published by PNAS. Each of these studies was multi-year and used focus groups, pretests and probability-based sample data. The time and money cost was very high. During the discussion one of the exhausted researchers involved in those studies asked how we economists could go from these great but unlikely-to-be-useful-for-policy-analysis (my words) studies to something that would be useful for policy analysis. The audience was stumped for a second and then I realized that I had an answer. The long-term answer, I think, is taking the lessons from these huge studies and developing benefit estimates with models from opt-in data. You can go do this within one year with opt-in data and a single pretest relative to 3-5 years for a major study. The test, I think, is whether the results from models using opt-in data is better than benefit transfer, which is how most policy analysis is being done.

I think the answer is yes (opt-in data models are better than benefit transfer). The second of the published AEPP articles above resulted from a pretest of the PNAS studies. It’s conclusion was that opt-in data wasn’t so bad. I’m hoping to contribute to the opt-in data is good enough for policy literature by thinking about the role of attribute non-attendance in analyzing opt-in data (more on this soon, I hope). We need more studies like these to convince a skeptical bunch of environmental economists and, especially, OMB that policy anlaysis will be improved if we don’t always rely on million dollar studies.