The paper is titled ''Contingent valuation versus choice experiments: a meta-analysis application exploring the determinants of the time for publication acceptance" [pdf] and here is the abstract*:
In this paper, we test whether the time it takes for a submitted paper to be accepted by the editor(s) is sensitive to the stated preference method used. Two methods are considered: the Contingent Valuation (CV) and the Choice Experiments (CE). A meta-analysis based on a sample of 129 papers published in Resource and Energy Economics, Ecological Economics and Environmental and Resource Economics between 2005 and 2011 is conducted. The dependent variable in the ordinary least squares regression model is the number of days between the submission of the paper and the acceptance of the paper, referred to as Time for Publication Acceptance, or TPA. The main results are that TPA is lower for CE papers than CV papers, especially for those that aim at improving the method which can be interpreted as a higher academic demand in the CE field. However, a convergence is observed over the years.
The time to publication is 68% lower for choice experiment papers. My theory is that choice experiments don't need to jump through the same narrow hoops that CVM papers must. Here is a previous rant on this subject.
Here is the first paragraph:
Adamowicz (2004) provided an overview of the future directions that the academic demand in the environmental valuation field may take by examining the number of publications between 1975 and 2003 for several valuation methods. According to the author, “the most significant advance in environmental valuation may be to move away from a focus on value and focus instead on choice behaviour and data that generate information on choices” (page 439). It implies that the Choice Experiments (CE) method may become more popular than the Contingent Valuation (CV). Whitehead (2011) confirmed such shift in the academic demand by examining the number of papers published between 1989 and 2010 for each method using the ISI database.
Romain Crastes and Pierre-Alexandre Mahieu, (2014) ''Contingent valuation versus choice experiments: a meta-analysis application exploring the determinants of the time for publication acceptance'', Economics Bulletin, Vol. 34 No. 3 pp. 1575-1599.
If the objective of graduate training in top-ranked departments is to produce successful research economists, then these graduate programs are largely failing. Only a small percentage of economics PhDs manage to produce a creditable number of publications by their sixth year after graduation. Even at the top five departments, it would be hard to argue that the bottom half of their students are successful in terms of academic research. The number of AER - equivalent papers of the median at year six is below 0.1 in all cases and is in fact zero in most. At the majority of the departments ranked in the top ten in conventional rankings (such as Coupé 2003), 60 percent of their students fail to meet this 0.1 AER -equivalent standard, and for the majority of the PhD graduates of the top 30 departments, 70 percent fail. A tenure standard of 0.1 AER-equivalent papers is roughly equal to publishing one paper in a second-tier field journal over six years. This record would not be enough to count as “research-active” in most departments, much less to result in tenure. ...
For graduate students in economics (and also potential graduate students), the message is that becoming a successful research economist is difficult. The good news is that one does not have to go to a top department in order to become a successful research economist. The bad news is that wherever one goes, only the very best of each class is likely to find academic success as defined by research publications. ...
Thus, students thinking about applying to PhD programs in economics would be well advised to have “Plan B’s” for every stage of the journey—including the possibility of not being accepted into a PhD program, the possibility of not completing the program, the possibility of not finding a suit able academic job, and the possibility of not receiving tenure. We hasten to add that there are many rewarding and worthwhile nonresearch and nonacademic career paths open to those who obtain masters or doctorate degrees in economics, and many students discover, either while in graduate school or during their untenured years, that they actually prefer these sorts of jobs to the academic life.
A couple of other quotes are worth excerpting. From the intro:
Economics PhD programs are primarily designed to produce research economists. There is little or no focus on training students to suit the needs of business or industry (Siegfried and Stock 1999). Our experience suggests that most students, especially at the better programs, enter graduate school planning to seek academic jobs, or at any rate, jobs that require research. We would also argue that students have a more-or-less common preference ordering over departments. In general, a student admitted to MIT or Princeton is unlikely to choose to go to Duke or Ohio State instead.
Teaching is not even mentioned in the first sentence. And, Duke and Ohio State? Burn!
And this from the conclusions:
However, at lesser departments, there is always a debate about whether it is better to hire lower-ranked graduates from top-ranked departments of economics, or the best graduates from lower-ranked departments. Our conclusion is that it is indeed worthwhile for lower-ranked schools to look outside the top-ranked departments for new hires, though only at the top students of such programs in general.
Some of us at "much lesser" departments have known about this for a long time. I'm most interested in the PhD program ranked #31 - all other PhD programs. The data (available at the link above) contain 7396 of these graduates and 59% (n=4356) do not have any publications** after 6 years. Of those with at least one publication, 82% have published less than two papers in second tier field journals (this is my definition of "research active" and my cutoff is 0.195). If you were at a much lesser department and trying to find a research active faculty member in the job market, there were only about 36 of these on the supply side of the market each year. I wonder if this is any different in in 2014?
Only eighty-nine (16%) of these 541 research-active PhDs published at least one AER-equivalent paper (1 paper in the AER or 10 in second-tier field journals). Here is a picture:
Incredibly, one 1991 graduate had 4.55 AER-equivalents in his/her first six years. Twelve others had two or more.
And going back to that line from the intro. The major activity of most PhD economists is teaching. Given these results it seems that PhD programs at departments 1-31 should spend a bit more time training teachers or, at least, paying it some lip service.
*Source: Conley, John P., and Ali Sina Onder. 2014. "The Research Productivity of New PhDs in Economics: The Surprisingly High Non-success of the Successful." Journal of Economic Perspectives, 28(3): 205-16.
**Here is information on the sample and measurement:
We start with a census of 14,299 economics PhD recipients from 154 academic institutions in the US and Canada who graduated between 1986 and 2000 compiled by the American Economic Association (AEA) and connect this to an EconLit database with 368,672 papers published between 1985 and 2006 in 1,113 peer-reviewed journals (including conference volumes to the extent that these are captured in EconLit).
My dissertation, written under the direction Glenn Blomquist in 1989, considered whether information about alternative environmental goods was missing from contingent valuation (CV) scenarios. Subsequently we received funding to consider whether the CV method could distinguish amongst quality differentiated environmental goods. The paper got beat around a bit but eventually Blomquist and Whitehead (1998) was published. It turns out to be my second most cited paper. Here is the abstract:
Elicitation of valid statements of contingent value requires survey participants who are familiar with the environmental resource change. A primary purpose of the contingent market must be to assure familiarity by providing information. Information about resource quality is important when incompletely informed respondents, say nonusers, perceive resource quality which diverges from true quality. Differences in perceived quality and true quality can be influenced as respondents learn from information in the contingent market. By presenting survey participants with information about four wetlands of varying qualities we test for information effects in a dichotomous choice contingent market for wetlands allocation. We find that information about quality is a determinant of willingness to pay for wetland preservation. Information about resource quality presented in contingent markets will result in more valid valuations of changes in allocations of environmental resources.
I don't think Glenn nor I walked away from Blomquist and Whitehead (1998) "suspicious" about the validity of the CV method. I've always wanted to be cited in the same parenthetical as Hausman (2012), for example, like this (Hausman 2012, Haab et al. 2013), but this one makes me feel yucky. For once, I wish one of my papers wasn't cited.
[Monday], EPA announced proposed performance standards for existing power plants under the Clean Air Act (also known as the existing source performance standards, or ESPS). Attention to the proposal is deservedly high, and it’s a monster of a rule (645 pages, at least in this unformatted version). That means general overviews are already out, and in-depth ones will have to wait. But there is still plenty to say today. Last week, I listed the five things I was most interested in seeing in the rule. Here’s how they came out. ...
The big headline is 30% emissions cuts from the power sector relative to 2005 by 2030. That’s quite large – over 500 million tons of CO2 per year. Most of those reductions (25%) appear to come by 2020, though a closer reading reveals states have some flexibility to push compliance out toward 2030 so long as they show they are making progress. ...
Also, remember that emissions have declined since 2005 – a good chunk of that 30% is already baked in.
The proposal doesn’t say a lot about flexibility, but that’s probably a good thing. ... this lack of detail gives freedom to states. And EPA does often suggest in the proposal that states may consider market-based approaches, and actively encourages states to band together and create regional programs by giving participating states more time to submit their plans. Carbon taxes, however, are not mentioned in the rule. Carbon-tax backers have argued that states will be unwilling to consider that option unless EPA were to explicitly bless it. We may see whether that prediction is correct.
3) Coal and Gas
The proposal does envision shifting from coal to gas – probably the lowest-cost emissions reduction opportunity available in the US economy (assuming, of course, that fugitive methane emissions from gas operations are not too high). In setting states’ targets, EPA assumes existing gas plants can be run more (up to 70% of capacity), displacing coal. States aren’t required to force this outcome, of course, but they can do so – and can do much more, including setting up trading programs between gas and coal. ...
In setting targets, EPA is considering efficiency improvements at existing coal plants and switching dispatch to existing gas plants as mentioned above. It’s also proposing to count new nuclear, renewables (as required by state portfolio standards), and a wedge of demand-side energy efficiency. States are free to push all of these and other opportunities for emissions cuts from the sector. Offsets and anything else that cuts CO2 outside the power sector are not considered. ...
5) Mass or Rate
The state-by-state targets EPA proposes setting are emissions rates. There’s even a table in the proposal showing the proposed targets for each state. Many of them are quite low, under 500 tons of CO2 per megawatt hour when a new, efficient gas plant might emit 800. But renewables, nuclear, and energy efficiency are included in the “adjusted” numbers in the table – neither the state fossil power sector nor any individual fossil plant would be expected to meet these standards. The targets vary greatly ...
Look for more on the proposal from us at RFF in the near future. ...
It is a pleasure to accept your manuscript entitled "Dubious and Dubiouser: Contingent Valuation and the Time of Day" in its current form for publication in Economic Inquiry.
Thank you for your fine contribution. On behalf of the Editors of Economic Inquiry, we look forward to your continued contributions to the Journal.
Sincerely, Dr. Yoram Bauman Co-Editor, Economic Inquiry email@example.com
Here is the abstract (I think Tim may have suggested the last sentence):
We collect contingent valuation data from 524 student survey respondents over a three day, 72 hour period. Data analysis of a hypothetical campus referendum focuses on time of day effects on willingness to pay for a renewable energy project. We find that subjects responding to the survey during the night time hours (i.e., between 12 a.m. and 6 a.m.) do not display the law of demand, offering theoretically invalid responses to questions with important policy implications. Results from this research may have serious implications for the contingent valuation method. In short, just like your father said, nothing good happens after midnight when using the contingent valuation method.
A “Miscellany" section is available in Economic Inquiry and intended for humor and curiosities. Economic Inquiry has a venerable tradition in humor, dating at least to the publication of Axel Leijonhufvud’s 1973 “Life Among the Econ.” ...
The referees don't think the paper is funny (at all) but there are a couple parts that still make me laugh out loud. Also, finally, I've managed to include "cromulent" in a paper.
Using data from almost 9 years of pageviews at this blog, I fit a trend line with an OLS regression model where the dependent variable is the 30 day moving average (the blue line) of page views as measured by Typepad (where x is the time period). Here are two contrasting 1000 day forecasts (the trend line beyond the blue). First, the R2 maximizing cubic function (the linear model has an R2 of near zero):
Second, here is the quadratic:
Which forecast would you choose? I'm hoping for the quadratic because the light is at the end of the tunnel.
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!
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Don't believe what they're saying
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