I was in College Station, TX at the end of March to talk about hypothetical bias of stated preference data to the folks in agricultural economics (here is the PDF of the PPT). I covered three recent papers that have ex-ante/ex-post SP/RP data which, as a whole, show (I argue):
ex-ante SP data is positively correlated with ex-post RP data;
there is hypothetical bias in ex-ante SP data (i.e., survey respondents say they'll do more of activity X than they actually end up doing);
the hypothetical bias can be adjusted (ex-ante) to reflect future behavior fairly accurately.
Here is the abstract from a new working paper which is the second of the three papers (here is a post describing the third):
One of the major criticisms of stated preference data is hypothetical bias. Using a unique data set of both stated and actual behavior we test for hypothetical bias of stated preference survey responses. We consider whether respondents tend to overstate their participatory sporting event behavior ex ante when compared to their actual behavior at different registration fees. We find that behavioral intentions accurately predicts actual behavior at a middle level of respondent certainty, over predicts actual behavior at a lower level of certainty and under predicts behavior at a higher level of certainty. This suggests that respondent uncertainty corrections can be used to mitigate hypothetical bias. Stated preference data can be used better understand actual behavior in situations where no data exist.
The first paper is being readied for submission to a third journal. We first sent it to Economics Letters where we received this confusing review:
Reviewer #1: "Criterion and Predictive Validity of Revealed and Stated Preference Data: The Case of Music Concert Demand"
The manuscript compares RP and SP visitation data to music concerts (in the NC area). As is clearly indicated by the title, the purpose is to explore the criterion and predictive validity of RP and SP data. The authors find evidence for predictive validity. The authors also recommend a method and modeling strategy for accurately predicting ex post RP visitations accurately. I beieve the authors have a good application to test validity and recommend a correction (i.e., music concert demand, provided by a not-for-profit organization). Similar to market goods, it has the benefit of experience and as such seems to be a good application for exploring the appropriateness of the correction. A concern is how applicable it is in a nonmarket setting.
I enjoyed the topic and believe the research question is important. As I read the manuscript, however, I tried to keep in mind an aim of Economics Letters, "...submit...important preliminary results, where perhaps the threshold for robustness, thoroughness or completeness of the analysis is not as high as it would be for a complete paper," and determine whether greater explanations were necessary in order to determine if the research provided important preliminary results. In other words, was the completeness there without being explicitly described.
Other comments: 1. Intercept Sampling information and response rates. I was a little confused. A 70% response rate is 91 (70% of 13 x ten concerts = 130 total surveys). There were a total of 83 usuable responses, so approximately only 8 of total responses were unusable. How was it possible to send a follow-up survey to 120 people when at most you had 91 original survey responses? 2. While I found the HB analysis interesting, what value does this add to the purpose of the study? 3. SP in the HB equation seems to describe something different than SP in LnQuantity equation. Confusing. 4. I was unable to confirm marginal effects Betax*Qbar with the results and means provided. This is also true for elasticities.
The first paragraph does a nice job of describing the paper and the author is correct, it may not be generalizable to a nonmarket setting. But, the papers that Hausman cites on hypothetical bias are from the marketing literature ... i.e., not generalizable in a nonmarket setting. The second paragraph doesn't seem to finish the thought. Am I to infer that the paper's results are not important and/or the "completeness" is not there? The "other comments" seem to be minor details. We next sent it to the Journal of Cultural Economics. It was rejected there because the sample is small (n=38). I can live with that reason but the Economics Letters review is mostly gibberish.
None of this should be a big deal, papers are rejected for all sorts of vague reasons. But the first time I had some data like this (ex-ante SP, ex-post RP) a referee tried to hold up publication because s/he thought hurricane evacuations were easy decisions to make (see the second half of this old post)! It would be great if I could get a referee who understands how difficult it is to collect this sort of data.
And none of this is in response to the response I received at TAMU. They seemed to understand. It didn't hurt that one of the authors of this paper was in the audience. And, thanks to everyone I hung out with for a great time!
Qingxin He, Winston-Salem State University, visited our department for a seminar last Friday. Her excellent paper, co-authored with Jonathan Lee from East Carolina University, is titled "The Effect of Coal Combustion Byproducts On The Pricing Strategy Upstream Industries." They find price discrimination in the wholesale coal market as a result of scubbers (or at least that is how my feeble mind understood it).
In lieu of useful feedback, we provided a car tour on a beautiful day (a big treat in February). And here is a picture to prove it.
I spent January 29 and 30 at the Sheraton Silver Spring participating in this Workshop (where there were a lot of great presentations from NMFS economists). I was given 10 minutes to talk on the "Panel Discussion on Management Needs, Future Directions." I tried to make four points (which is pretty much my research agenda if I can ever focus on NMFS data).
1. Increased use of existing MRIP data is needed
The councils need fairly mundane valuation models, tailored geographically, for management decisions. A basic NRUM with species groups with the most recent MRIP data would be a major advancement. It would provide information on the value of catch that could be used in discussion of recreational quota, bag limits, etc. There is a lot of data, it is generally good (in the most basic sense), and it is underutilized.
2. Choice experiments are not a panacea for contingent valuation
NMFS has become reliant on choice experiment surveys for valuation. The major benefit of choice experiments in the marine recreational fishing context is that they are good for valuing individual characteristics of the fishing trip and getting information on the no trip option. Other than their great expense, the drawbacks of using choice experiments in the recreational fishing context is not well known.
Choice experiments haven’t faced the scrutiny of CVM surveys. In my opinion, they quickly rose to prominence as an alternative to CVM in the 1990s by assertion. I say that because choice experiments potentially face the same sort of problems as CVM (e.g., anchoring, ordering, scope, etc) but the profession has not demanded examination of these issues.
CVM surveys have been shown to suffer from anchoring bias, ordering effects and incentive incompatibility in multiple choice questions. The reaction has been to ask a single question. Similar effects have been found with choice experiments with no apparent reaction. Only a few studies have considered these problems with choice experiments.
CVM surveys have been criticized for passing internal scope tests but not split-sample external scope tests. The reaction has been to disparage CVM applications that don’t pass external scope tests. With the exception of a few studies, choice experiments routinely pass internal scope tests with no criticism that these are weaker tests.
The use of choice experiments by the NMFS is to be commended. But more recognition of their challenges is warranted.
3. Major improvements can be the MRIP with simple add-on surveys
MRIP asks for the number of trips taken within the state of intercept. The linked model can be used to assess changes in catch and site access on the overall number of trips. But this is inconsistent with the RUM because the RUM does not have an opt-out alternative. A negative change in fishing characteristics would cause trips to reallocate to other sites, modes and target species while the overall number of trips stays the same.
Simulating the effect of these negative changes in a linked model is inconsistent and biases the estimate of the change in the number of trips. The bias cannot be signed due to the forcing of trips. If no trip would be preferred and the angler is required through simulation to travel elsewhere then the bias is positive (i.e., too many trips would result from the linked model simulation). If the angler would prefer a trip to another site then the bias is negative (i.e., too few trips would result from the linked model simulation).
The basic RUM would greatly benefit from two additional pieces of data in add-on surveys:
A CVM question that asks if the most recent trip were $X higher, would you still have taken the trip? This would provide an opt-out option for modeling changes in the number of trips and/or,
Site specific RP trip data would allow for estimation of a repeated RUM (e.g., this was done in the 1997 SE add-on).
Both of these questions are relatively easy to implement in a short add-on survey. My guess is that this would be much cheaper than the choice experiment surveys.
Another extension is to ask contingent behavior (SP) follow-up questions. Contingent behavior trip questions are also cheap to design and feasible to implement. Questions about changes in bag and size limits, trip cost and expected catch would be valuable.
4. Joint estimation of MRIP and NMFS choice experiment data would strengthen both models
It isn’t clear if the coefficient on the cost variable in choice experiments is similar to the RUM travel cost estimate, which is probably more reliable (if it is measured accurately). Since the catch value is an inverse function of the cost coefficient, a biased cost coefficient could have large effects. It should be straightforward to combine the MRIP data to the NMFS choice experiment data and jointly estimate the model to determine if the cost coefficients are different. This was the point made by Adamowicz et al. (1994) in JEEM that helped launch choice experiments in environmental economics but joint estimation of choice experiment and revealed preference data has largely been ignored since.
Also, simple preference questions can be jointly estimated with MRIP data. Single questions on the 1997 Southeast and 1998 Pacific add-on MRFSS economic surveys could be used to jointly estimate management preferences.
The AERE annual meeting was in Asheville, NC a couple of years ago. I went to too many sessions so had to go back last week and do some things that I missed. On my New Year's "hike" I saw five waterfalls, Looking Glass, Moore Cove, Sliding Rock, Slick Rock and Jackson, in the Pisgah Ranger District. Here is a shot of Jackson:
This is an email I just sent to the RESECON listserv:
As you may know the Western Economic Association International meetings are being held in Denver, CO June 27-July 1, 2014 (http://www.weai.org/conferences.html). Since this conflicts with the WCERE there will be no AERE organized sessions at the WEAI this year. But that doesn't mean that non-sponsored sessions can't be organized. That double negative means that, since I'm not able to go to the WCERE and would like to attend an economics conference, I would like to improve the experience by organizing one or more environmental and resource economics sessions this year at the WEAI (and submission fees are waived for organized sessions).
So, if you are also unable to attend the WCERE meetings and would like to attend the WEAI meetings please let me know ASAP. The deadline for individual papers is December 15 but the deadline for sessions is not until February 1. I'd like to know if an organized session is possible before I miss the 12/15 deadline.
P.S. Please don't hedge your WCERE bet with feigned interest in the WEAI, I'm hoping for a very low dropout rate (i.e., I'm hoping for great sessions at the WEAI!). In other words, if you are planning a WCERE submission, please don't submit to both.
The Lone Star state may be warning Elon Musk “Don’t mess with Texas” thanks to its newest Tesla (NASDAQ:TSLA) sales ban, but Musk doesn’t exactly seem to be running scared. In fact, his company just recently erected a new Supercharger station right on Texas grounds, conveniently halfway between Austin and San Antonio and behind the San Marcos Outlet Mall.
YNN reports that the Supercharger is the first of its kind in Texas, marking just one more benchmark in Tesla’s ultimate goal of constructing charging stations that span the nation’s roads and thus allowing Model S drivers to make coast-to-coast road-trip plans without having to worry about an uncharged sedan.
As of now, there are 18 Superchargers across the country, clustered mostly on both the West and East Coast. But this new charging station, smack-dab in the heart of Texas, proves that more and more Superchargers will soon make an appearance, whether U.S. states agree with Musk’s selling techniques or not.
Musk has his eye on Texas because the Lone Star state was the first of its kind to ban sales of the Model S within its borders. Texas lawmakers don’t like that Musk has removed the middlemen in the car-buying experience; the electric vehicle maker instead sells it product directly to consumers.
I took some time off and visited family in Kentucky last week. It was raining when we were packing up on Sunday so I moved the car into the garage to tie everything up on top. The angle isn't the best, but do you notice anything odd about this picture?
Right on, we couldn't get out of the garage. I had to take down the bags, pull the car out and tie them back up there.
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... the Environmental Economics blog ... is now the default homepage on my browser (but then again, I guess I am a wonk -- a word I learned on the E.E. blog). That is a very nice service to the profession. -- Anonymous
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