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.