Environmental Economics

Teaching consumer surplus with the Becker-DeGroot-Marschak method in an environmental and resource economics course

The other day in my intro environmental and natural resource economics class we used the BDM method to elicit willingness to pay (WTP) values for an App State travel tumbler (I paid $26 with a faculty discount at the university bookstore). I explained the BDM with these slides [Download BDM] and each student had a "payment card" for revealing their WTP [Download BDM-WTP]. The average WTP was $6.23 (n=26). I entered the WTP values into Excel, sorted them from highest to lowest and plotted them along with the randomly chosen price ($7). At this price, 12 of 26 students would have purchased the tumbler and enjoyed a consumer surplus of $45 (CS = WTP - price).

As a preview of what we are going to do later in the semester, I simulated a dichotomous choice stated preference exercise. For each WTP value I randomly chose a price that ranged from $2 to $14 (average = $5.69) and simulated whether the consumer would purchase the tumbler or not. Fifty percent of the consumers would purchase the product. I then estimated a linear probability model: Pr(purchase=1) = 0.711 - 0.0372 x Price. Plotting this line and calculating the consumer surplus area of the triangle yields a CS estimate of $6.81 -- very close to the actual CS average. I told them that this valuation approach is called the dichotomous choice WTP survey approach and is used in E&R economics (and marketing) to estimate WTP values for environmental amenities and consumer goods.

I randomly chose one of the student's WTP sheets and the WTP was less than $7. Then I chose another and the WTP was $15. This student paid me $7 and is, apparently, enjoying $8 of consumer surplus. 

I think I've convinced many of the students in this class that consumer surplus is equivalent to getting a "good deal". Now the trick is to convince them that WTP measures the value of nonmarket goods ... too be continued.

Reference

Schubert, Jens. "Using the Becker-DeGroot-Marschak Mechanism to Teach Willingness to Pay and Consumer Surplus." Journal of Economics Teaching 8, no. 1 (2023): 1-11. [URL]

How hard is it to proofread your paper after you use ChatGPT to write it?

From Nature News (Scientific sleuths spot dishonest ChatGPT use in papers) via Retraction Watch Weekend Reads [brackets added below]:

Searching for key phrases picks up only naive undeclared uses of ChatGPT — in which authors forgot to edit out the telltale signs — so the number of undisclosed peer-reviewed papers generated with the undeclared assistance of ChatGPT is likely to be much greater. “It’s only the tip of the iceberg,” ["scientific sleuth Guillame"] Cabanac says. (The telltale signs change too: ChatGPT’s ‘Regenerate response’ button changed earlier this year to ‘Regenerate’ in an update to the tool).

Cabanac has detected typical ChatGPT phrases in a handful of papers published in Elsevier journals. The latest is a paper that was published on 3 August in Resources Policy that explored the impact of e-commerce on fossil-fuel efficiency in developing countries. Cabanac noticed that some of the equations in the paper didn’t make sense, but the giveaway was above a table: ‘Please note that as an AI language model, I am unable to generate specific tables or conduct tests …’

I wanted to see it for myself and I bet you do too (at the bottom of the screenshot):

Let's say you want to use ChatGPT to help write your papers (because writing you own papers is soooooo hard). First, you must admit it in your acknowledgements or your risk retraction. Second, actually read your own paper and edit this nonsense out. 

Teaching opportunity cost with a Veconlab experiment: comparing F2F and online classes

My go to classroom experiment has been Veconlab's supply and opportunity cost experiment for at least a decade (here is a 2016 post). Here is the abstract from Holt et al. (2010):

This paper describes an individual choice experiment that can be used to teach students how to correctly account for opportunity costs in production decisions. Students play the role of producers that require a fuel input and an emissions permit for production. Given fixed market prices, they make production quantity decisions based on their costs. Permits have a constant price throughout the experiment. In one treatment, students have to purchase both a fuel input and an emissions permit for each production unit. In a second treatment, they receive permits for free and any unused permits are sold on their behalf at the permit price. If students correctly incorporate opportunity costs, they will have the same supply function in both treatments. This experiment motivates classroom discussion of opportunity costs and emission permit allocation under cap and trade schemes. The European Union Emissions Trading Scheme (EU ETS) provides a relevant example for classroom discussion, as industry earned significant “windfall profits” from free allocation of emissions permits in the early phases of the program.

I'm teaching an intro to environmental and resource economics class to mostly non-majors. For the third semester in a row I'm teaching online (n=45) and in-person (n=26). Both classes participated in the experiment on the second day of class. Here is the estimated linear probability model with Pr(sale=1) as the dependent variable, PRICE ranges from 1.5 to 8.5, COST is the marginal cost of production equal to 1, 3, 5 for units 1, 2, 3 in each of the 16 rounds, and GRAND is equal to 1 when permits are grandfathered and 0 when there is the permit price is $3. Through the magic of in-person teaching I stopped the experiment after four rounds of mostly ignoring opportunity cost and explained what was going on (DEBRIEF=1, 0 otherwise). The model is OLS with clustered standard errors (n = 20, t = 48 with the face-to-face data; n = 33, t = 48 with the online data):

Looking at the constants, the online class was more likely to sell the product with a 40% baseline probability (vs 26% in the F2F class). The F2F class put more weight on the price and cost. The price and cost coefficients should be equal since a $1 change in each effects profits the same way. But, in both classes more weight is placed on the cost variable. The F2F class ignored opportunity cost more than the online class. The debrief reduces much of the overproduction in the grandfathered rounds. But, overproduction is not much lower than for the online class after the debrief. The coefficient of determination (R2) tells me that the F2F class paid more attention to the variables than the online class.  

Here is my attempt to turn this experiment into a journal article: https://www.env-econ.net/2023/09/new-working-paper-with-tanga-mohr.html. 

New working paper (with Tanga Mohr)

The title is "External Validity of Inferred Attribute Non-Attendance: Evidence from a Laboratory Experiment with Real and Hypothetical Payoffs". Here is the abstract:

We consider differences in hypothetical and real payoff laboratory experiments using attribute non-attendance methods. Attribute non-attendance is an empirical approach that measures and accounts for when survey respondents ignore attributes in stated preference surveys. We use attribute non-attendance methods with data from an emissions permit experiment with real and hypothetical payments. Our conjecture is that attribute non-attendance may be more pronounced in hypothetical sessions and, once accounted for, hypothetical decisions and real decisions influenced by monetary payoffs will be more similar. In both treatments we find that the effect of the cost of an emissions permit on behavior differs if the cost is implicit or explicit. In inferred attribute non-attendance models with the real treatment data we find two classes of respondents with different behavior but no evidence of attribute non-attendance. With the hypothetical treatment data we find two classes of respondents with different behavior and evidence of attribute non-attendance on two of the four choice attributes.

And yes, it is the first paper where I've used lab experiment data. I received $2000 from the Education and Technology Fund to run the experiments as part of an upper level environmental economics class during Spring 2020. Students helped conduct the experiments in the lab and used it to write papers. Covid diminished the learning experience but the data are still good. We supplemented the lab experiment data with online experiment data the next year. I'll be shocked if a referee doesn't ask us to estimate the models separately.

Here is the link: https://econpapers.repec.org/paper/aplwpaper/23-05.htm

I've wasted about an hour fighting with Editorial Manager

If you submit a paper to the [redacted] journal and follow the submission guidelines:

Then the journal will kick the submission back to you and say:

Provide conflict of interest statement in the blinded manuscript.

So now the information is on the title page and in the blinded manuscript. And, I've wasted about an hour fighting with Editorial Manager. 

New book: Discrete Choice Experiments Using R

From Springer: 

This book delivers a user guide reference for researchers seeking to build their capabilities in conducting discrete choice experiment (DCE). The book is born out of the observation of the growing popularity – but lack of understanding – of the techniques to investigate preferences. It acknowledges that these broader decision-making processes are often difficult, or sometimes, impossible to study using conventional methods. While DCE is more mature in certain fields, it is relatively new in disciplines within social and managerial sciences. This text addresses these gaps as the first ‘how-to’ handbook that discusses the design and application of DCE methodology using R for social and managerial science research. Whereas existing books on DCE are either research monographs or largely focused on technical aspects, this book offers a step-by-step application of DCE in R, underpinned by a theoretical discussion on the strengths and weaknesses of the DCE approach, with supporting examples of best practices. Relevant to a broad spectrum of emerging and established researchers who are interested in experimental research techniques, particularly those that pertain to the measurements of preferences and decision-making, it is also useful to policymakers, government officials, and NGOs working in social scientific spaces.

I'm too old to learn how to estimate DCEs with R but this looks like a good book for someone who wants to do that or teach it to grad students. 

Pageviews since the reboot

We made the big announcement that we would try to post more here in January 2023. We average 400 visitors from January through May. That is 600 fewer relative to when blogs were big in  economics (i.e., before Twitter took over the conversation). Summer followed pattern we were accustomed to with only a fraction of the academic semester average number of visitors. Except, except for August 11 when we inexplicably enjoyed 3420 visitors. There was a post on August 20 but nothing to explain that spike -- it received 692 views, 13 likes and 1 retweet on Twitter. I'm not tech savvy enough to figure out where all these came from. I'm just concluding that was a weird day.

Is my online questionnaire too long?

I sent the questionnaire to an email list provided by the client* and received this email:

Started answering but too long. 

I replied:

Thanks for letting me know.

I sent the email on Monday and the current response rate is 36%. The completion rate is 95% so maybe up to 5% of the respondents think the questionnaire is too long.

Here is the distribution of minutes taken to complete the survey:

The median is 8 minutes and the mean is 10. Six respondents took more than 20 minutes to complete it and one took over an hour (i.e., got started, had dinner, and then finished).

The paperback is out

The price is 25% of the hardback here.

 

Climate adaptation in the lobster fishery in the Gulf of Maine (but don't get too excited)

Some people will get very excited by the title of this post (before the parenthetical) but don't for one minute think this adaptive behavior isn't costly (Heating Waters Force Change in Industries That Depend on the Ocean):

In Maine, lobsters are heading north and some lobstermen are moving into kelp farming. ...

Marine heatwaves are expected to become stronger and more frequent, according to a consensus of oceanographers and climate scientists.

One of the fastest-warming waters is the Gulf of Maine, forcing lobstermen to change tactics to save their businesses.

Summer surface temperatures between July and September in the gulf have risen four times the global ocean average since 1982. The entire region is warming more rapidly than 97% of the ocean’s surface because of overall climate warming and a shift in local currents, according to the Gulf of Maine Research Institute.

The warming is making life more unpredictable for the $388 million lobster fishing industry in Maine. The U.S. lobster catch has moved 162 miles northward and nearly 70 feet deeper over the past 50 years, according to the National Marine Fisheries Service, as the crustaceans seek refuge in colder water. The Maine lobster catch has fallen 26% since a record catch of 132 million pounds in 2016.

Some harvesters in the state are looking for new species, such as black sea bass, tuna or crabs.

Steve Train, a third-generation lobsterman in Casco Bay, Maine, has become a farmer. He started growing kelp–something still plentiful in the area–offshore of his property to earn money during spring months when lobstering is traditionally slow.

Train says kelp is no substitute for lobsters, still the most lucrative single-species commercial fishery in U.S. waters. But it can help as Maine’s coastal environment changes.

“The people who survive are going to have to be diversified so they can take the cream out of a few different fisheries and have a paycheck year round,” Train said as he pulled up a string of lobster traps onto his 32-foot boat Marcia, a few miles from the Portland wharf where he sells his catch. ...

In Casco Bay, Steve Train has watched Maine’s lobster population shift north, driven by changes in bottom water temperatures and the supply of the tiny animals that are the main food of baby lobsters.

Over time, Train has been moving his traps to deeper water and said he has noticed a faster temperature increase in the past five years. “I started setting gear in June where I thought the lobsters would be in August or September,” he said. “Everything the lobsters do is by water temperature.”

Train and other lobster fishermen own licenses that are tied to specific fishing grounds. So there are limits on how far north or offshore they can set their traps.

Train, 56, says his kelp-farming venture isn’t as easy as he initially thought. “Between the moorings, the storms, the tide, and the stretch of the line, there’s a lot of maintenance,” Train said. “But now I have another month or two filled in for cash flow.”

He said he is figuring out the best direction to suspend ropes in the water that are embedded with kelp seeds to avoid tidal currents that could damage his harvest. The seeded ropes mature into an underwater forest. He expects to be profitable this year, while expanding from 4 acres to 10 acres.

Each spring, Train collects wide fronds of brownish-yellow kelp and sells them to Atlantic Sea Farms, a Biddeford, Maine-based company that processes seaweed into kelp burgers, smoothie cubes, and pet-food additives. It has signed up 30 lobster fishermen across Maine since it launched in 2018, and another 19 are in line for a state license, according to CEO Briana Warner.

“We’re building an entirely new market,” said Warner, who sold 30,000 pounds of kelp products last year to grocery and restaurant chains.

Train said lobstermen he has known for decades are beginning to consider a future that will reward adaptability as waters continue to warm. “Even the people that are in denial are a little concerned,” he said.

So, the lesson from this case study is that, yes, people will adapt to climate change but the net benefits of their new behavior is lower with the climate constraint than business as usual pre-climate change. Economists who promote adaptation as a magical fix are doing a disservice. 

*****

And, inexplicably, in the middle of the article someone at the WSJ decided to question whether climate change is happening:

There are other possible explanations in addition to climate change and the El Niño/La Niña cycle. New pollution rules have cut airborne sulfur aerosol particles released by commercial ships over parts of the ocean, clearing the air and allowing more sunlight to reach the ocean surface. That in turn might be heating the water along some shipping routes, although the amount is in dispute, according to several recent estimates.

In January 2022, an underwater volcano near Tonga blasted 50 million tons of water vapor into the stratosphere. Some researchers believe that vapor might be acting as a planet-warming greenhouse gas and nudging up ocean temperatures. Both theories are still under investigation, and their overall impact is up for debate.