Environmental Economics

"Writing the introduction to a journal article"

The introduction is the hardest part to write. Pat Thomson:

The introduction can actually be thought of as a kind of mini-thesis statement, with the what, why and how of the argument spelled out in advance of the extended version. The introduction generally lays out a kind of road-map for the paper to come. It also lets the reader know broadly about the kinds of information and evidence that you will use to make your case in the paper.

Writing an introduction is difficult. You have to think about:

• the question, problem or puzzle that you will pose at the outset, as well as
• the answer, and
• how the argument that constitutes your answer is to be staged.

At the same time, you also have to think about how you can make this opening compelling. You have to ask yourself how you will place your chosen question, problem or puzzle in a context the reader will understand. You need to consider: How broad or narrow should the context be – how local, how international, how discipline specific? Should the problem, question or puzzle be located in policy, practice or the state of scholarly debate – the literatures?

Then you have to consider the ways in which you will get the reader’s attention via a gripping opening sentence and/or the use of a provocation – an anecdote, snippet of empirical data, media headline, scenario, quotation or the like. And you must write this opener with authority – confidently and persuasively.

Writing a good introduction typically means “straightforward” writing. Not too many citations to trip the reader up. No extraordinarily long sentences with multiple ideas separated by commas and semicolons. Not too much passive voice and heavy use of nominalisation, so that the reader feels as if they are swallowing a particularly stodgy bowl of cold, day-old tapioca.

via blogs.lse.ac.uk

There is good stuff before and after this excerpt.

Hat tip: Retraction Watch

I can't wait to join RePEc's list of economists on Twitter! pwh15 @RePEc_signup

RePEc has concluded that economics blogs are not going to be an effect platform for discussion of research:

RePEc has already tried to capture what is happening in the economic blogosphere. EconAcademics is a website that aggregates the discussion of economic research while monitoring about 1000 blogs. The idea here is to find those blog posts that link to research indexed on RePEc, on the presumption that they discuss research or use research to make a point. This is in contrast to a lot of the discussion of economics that very quickly veers into politics with little backing from actual research. EconAcademics thus makes those blogs better known that are more “seriously” into economics. The site can also be used to find interesting material for the classroom or simply to broaden once [sic] interests. We hoped also that this would encourage more discussions in the comment sections of those blogs. Anecdotally, this does not seem to have happened. Economists seem too busy to engage in any significant way in such discussions, with few exceptions. This is especially true when it comes to commenting papers. Blogs have not become a medium where a discussion can drag on for days (although it could have), as a careful reading of the papers would require.

So they are giving it a shot with Twitter:

What about Twitter? It would seem that the instantaneous nature of Twitter, as well as the 140 character limit of a tweet, would make it even more difficult to have serious and thoughtful discussions about research. It appears that economists recently have been warming up to the idea, and many prominent ones have joined Twitter and contribute thoughts on policy and current research. The difference with blogs is that Twitter somehow engages more discussion, and it also prompts interactions between people who would never have interacted otherwise: it makes everyone accessible. That said, one can also simply be lurking without intervening and learn from the discussion or the alerts. As we find from analyzing traffic to RePEc, Twitter can drive substantial readership to some papers. Another example is the previous post on this blog, that got substantial readership, almost all through Twitter.

How can RePEc help here? There are two ways. The first is that every new working paper that is announced through the NEP mailing lists is now also disseminated through Twitter (see for a list of all the available feeds one can follow). This has been in place for close to a year and has so far gathered a following of about 3000 users with a steady flow of further dissemination through retweets.

The second is that it is now possible for authors to add their Twitter account to their IDEAS author profile, thereby making their Twitter time line easier to find. All they need to do is to tweet their RePEc Short-ID to @RePEc_signup. This allows also to compile a list of economists present on Twitter, which we hope will grow quickly.

The key phrase here is that "Twitter somehow engages more discussion" and the key word is "somehow." I'd rather comment on a blog post (with a keyboard).

Using big data for fish harvest estimation

David Carter, Scott Crosson, and Christopher Liese in PLOS ONE: 

We introduce a regression approach that uses information on fishery-related internet search volume to provide more timely intraseasonal predictions of recreational harvest. There is a growing literature showing that the internet search volume on a particular topic (e.g., unemployment insurance) can be used to predict current levels of policy-relevant variables (e.g., unemployment rates) [4]. Importantly, these predictions are not forecasts of future conditions, but rather “nowcasts” of current conditions. In the words of Choi and Varian [5] the goal is to “predict the present.” Nowcasting has value because traditional methods of compiling statistics from surveys or official records takes time. Any lag between the time that decision makers need information and the time that the information is available increases uncertainty, which increases the risk of poor decision making. If nowcasting can reduce this uncertainty, the quality of decisions can be improved [5–9]. Nowcasting attempts to arrive at an estimate sooner, but in no way replaces traditional methods, which remain the standard against which nowcasts are judged and continuously re-calibrated.

We demonstrate the harvest nowcasting approach with the Gulf of Mexico recreational red snapper fishery. This fishery has been particularly difficult to manage with progressively shortening seasons in the presence of changes in effort and an increasing average fish size. The recreational sector has overharvested red snapper in every year from 2007 to 2013 with the exception of 2010, when the Deepwater Horizon (DWH) oil spill forced a mandatory closure of prime fishing grounds during the busy summer season.

NOAA fisheries forecasts recreational harvest of red snapper and other key species several months in advance in order to set fishing seasons [10]. Forecasts are based on trends in historical catch rates and fish weight by fishing sector (private, for-hire). The agency does not regularly monitor the harvest of red snapper within the season because the season is shorter than the data reporting period. However, there have been times when managers reopened the fishery later in the year. In these cases there was a need for information on the cumulative level of harvest before the harvest data were available. For example, in 2013 following a new stock assessment, the recreational quota increased after the summer season closed and there was an interest in re-opening the season in the fall. In this case, there was a need to determine whether the new quota had been exceeded during the original season. Our proposed approach is designed to warn fishery managers of pending quota overages within the year by nowcasting harvest using a regression model including data on internet searches during the summer months when fishing activity peaks.

This may be a big contribution to a huge problem, at least for popular species. Imagine a recreational fishing harvest quota. The NMFS estimates harvest from the MRIP creel survey in two month waves over the fishing season. Data collection, entry and analysis takes time (especially relative to commercial fishing statistics with centralized reporting at dockside). By the time the NMFS figures out that the recreational quota has been it plenty of time has gone by so that the quota might be significantly exceeded.

Here is an image of the impressive results:

Here is the reference:

Carter DW, Crosson S, Liese C (2015) Nowcasting Intraseasonal Recreational Fishing Harvest with Internet Search Volume. PLoS ONE 10(9): e0137752.

 

The sad reality of gender bias among college students

Graphic of the Day (From The Economist):

Conclusion: Economics professors are horribly brilliant (or is it brilliantly horrible?)

My experience is that students either love or hate economics.  The middle is very small.  So the contrast here doesn't surprise me. 

But I think there is a more important, and troubling, conclusion to draw from this:  Female professors are universally more horrible and less brilliant than male professors. It is time to recognize the reality and ban females from the classroom. We need to stop this unjust and unfair treatment of students and reform our education system to ensure our students are getting the education they deserve, from the professors best equipped to teach them.  The male professors.

This ends a public service message from Donald Trump.

Of course I am being facetious. Students rate female professors lower independent of the quality of teaching.  Why? Well...I don't know why.  But there is evidence that this is the case. 

A faculty member in our department (who happens to be female), forwarded me this article from NPR last week.  The conclusion is stark:

A new study argues that student evaluations are systematically biased against women — so much so, in fact, that they're better mirrors of gender bias than of what they are supposed to be measuring: teaching quality.

The evidence?

In this paper, the team ran a series of statistical tests on two different data sets, of French and U.S. university students.

The French students were, in effect, randomly assigned to either male or female section leaders in a wide range of required courses. In this case, the study authors found, male French students rated male instructors more highly across the board.

Is it bias? Or were the male instructors, maybe, actually, on average, better teachers? (It's science; we have to ask the uncomfortable questions.)

Well, turns out that, at this university, all students across all sections of a course take the same, anonymously graded final exam, regardless of which instructor they have.

This offers the chance to look at one dimension of actual instructor quality: Presumably better section leaders would help students get better grades on the same exam. In fact, they found, the students of male instructors on average did slightly worse on the final.

Overall, there was no correlation between students rating their instructors more highly and those students actually learning more.

The American case was a little bit different. Here, the authors performed a new analysis of a clever experiment published in 2014. Students were taking a single online class with either a male or female instructor. In half the cases, the instructors agreed to dress in virtual drag: The men used the women's names and vice versa.

Here, it was the female students, not the males, who rated the instructors they believed to be male more highly across the board. That's right: The same instructor, with all the same comments, all the same interactions with the class, received higher ratings if he was called Paul than if she was called Paula.

And that higher rating even applied to a seemingly objective question: Did this teacher return assignments on time? (The online system made it possible to ensure that promptness was identical in every case.)

Conclusions: 

1. French male students and American female students like male professors.
2. Student evaluations of teaching quality suck.  But I don't know of a better alternative. In evaluating faculty teaching, I use student evaluations to identify trends and possibly identify outliers, and nothing else. 
3. Gender bias is real.  It is troubling. And embarrassing. 

I'm the proud father of two smart, kind, athletic daughters (and a son, but he's a different story...I'm kidding!).  Both of my daughters excel in math and science.  My wife and I have both preached to our daughters that their gender doesn't matter.  If they like doing something, do it, and they will be rewarded for it. 

I fear I am lying to my daughters.  

And that makes me sad. 

 

A price floor on gas and diesel in China

Barely a month after world leaders signed a sweeping agreement to reduce carbon emissions, the global commitment to renewable energy sources faces its first big test as the price of oil collapses.

Buoyed by low gas prices, Americans are largely eschewing electric cars in favor of lower-mileage trucks and sport utility vehicles. Yet the Obama administration has shown no signs of backing off its requirement that automakers nearly double the fuel economy of their vehicles by 2025.

In China, government officials are also taking steps to ensure that the recent plunge in oil prices to under $30 a barrel does not undermine its programs to improve energy efficiency. Earlier this month, the country’s top economic planning agency introduced a new regulation, effective immediately, aimed at deterring oil consumption. ...

In China, the world’s biggest greenhouse gas emitter, the government implemented a new rule that no matter how low world crude oil prices may fall, the price of gasoline and diesel will continue to be set as though the world price of oil were still $40 a barrel. The goal is to prevent gasoline and diesel from becoming so cheap that China’s citizens would start consuming it indiscriminately.

China’s heavily state-owned refining industry will also not be allowed to keep the extra profits from buying crude oil cheaply and selling gasoline and diesel as though the crude oil still cost $40 a barrel. Instead, the Chinese government will take the extra refining profit margin and put the money into a special fund for energy conservation and pollution control.

via www.nytimes.com

Here is an illustration of what might happen (oil prices are in parentheses):

The typical price floor would create a surplus as firms overproduce given the price signal and compete for the scarce consumers (the dotted line). In this case, since oil refineries can't keep the excess profits (area a+b) they might produce at the intersection of demand and supply at (30) and keep the excess production as inventory for when oil prices go back up.

What am I missing?

The price of oil (adjusted for inflation)

Source: http://www.nytimes.com/interactive/2016/business/energy-environment/oil-price-supply-demand-imblance.html

And here is the explanation:

United States production has surged in recent years as the shale boom took off. That has helped create a glut of oil as major producers like Saudi Arabia continue to pump at high levels.

Daily demand and supply: Shortage

We're a bit south of it*:

The storm — called Snowmaggedon2016 on Twitter, and named Winter Storm Jonas by the Weather Channel — could approach the 28 inches in January 1922 that ranks as Washington’s snowiest storm and is likely to easily surpass the highest recent snowfall, 17.8 inches that fell in February 2010.

But Mr. Otto said the region west of Washington, including parts of Virginia, West Virginia, southeastern Pennsylvania and northern Maryland, will form the “bull’s-eye” of the blizzard, with as much as 30 inches of snow expected there. ...

In suburban Bethesda, traffic was clogged with cars trying to squeeze into the parking lot of Strosnider’s, a local hardware store. People in knit caps and puffer coats crowded the aisles in search of batteries, flashlights, salt and snow shovels. One woman, Susan Schwartz, gave up and left, going to another store in nearby Kensington, which was by then devoid of storm supplies.

“There were no shovels and no salt to be found,” she said.

via www.nytimes.com

Test question: In a normally functioning market (devoid of the social stigma of "price gouging"), what would have happened to the price of shovels in advance of the storm? Explain.

Answer: The price would have risen in response to the increase in demand. Those most willing (and) able to pay for shovels would have gotten them. The hardware store should have scheduled a sealed-bid second price auction to extract as much surplus as possible from the poor suckers who don't normally need shovels.

*Note: We've only gotten 8"-10" and are expecting a few more which was much less than was forecast by the local weather guy on Thursday. There are a lot of snow weenies around here and they were spittin' mad (on facebook) that the forecaster dared to be wrong.

Am I part of the reason behind rising college costs?*

By contrast, a major factor driving increasing costs is the constant expansion of university administration. According to the Department of Education data, administrative positions at colleges and universities grew by 60 percent between 1993 and 2009, which Bloomberg reported was 10 times the rate of growth of tenured faculty positions.

Continue reading the main story Write A Comment

Even more strikingly, an analysis by a professor at California Polytechnic University, Pomona, found that, while the total number of full-time faculty members in the C.S.U. system grew from 11,614 to 12,019 between 1975 and 2008, the total number of administrators grew from 3,800 to 12,183 — a 221 percent increase.

The rapid increase in college enrollment can be defended by intellectually respectable arguments. Even the explosion in administrative personnel is, at least in theory, defensible. On the other hand, there are no valid arguments to support the recent trend toward seven-figure salaries for high-ranking university administrators, unless one considers evidence-free assertions about “the market” to be intellectually rigorous.

What cannot be defended, however, is the claim that tuition has risen because public funding for higher education has been cut. Despite its ubiquity, this claim flies directly in the face of the facts.

via www.nytimes.com

*I like to think of myself as a faculty member with administrative responsibilities. I still teach (although slightly less than I did five years ago), I still produce research (although slightly less than I did five years ago) and I still engage in outreach (I'm still blogging). I have no plans to add myself to the growing rolls of administrators at levels higher than me, although as an economist I have trouble blaming those who choose that path for doing so. I'm sure if someone were to offer me a sizable increase to take on a true adminsitrative position, I would give it its due consideration. In fact, let's give it a try.

Someone make me an offer.

Environmental racism in Flint

This is one of those environmental issues where economics is not very helpful:

If Flint were rich and mostly white, would Michigan’s state government have responded more quickly and aggressively to complaints about its lead-polluted water?

The 274 pages of emails released by Gov. Rick Snyder this week on Flint’s water crisis included no discussion of race. Instead, they focused on costs relating to the city’s water supply, questions about scientific data showing lead contamination and uncertainty about the responsibilities of state and local health officials.

But it is indisputable that in Flint, the majority of residents are black and many are poor. So whether or not race and class were factors in the state’s agonizingly slow and often antagonistic response, the result was the same: Thousands of Flint’s residents, black and white, have been exposed to lead in their drinking water. And the long-term health effects of that poisoning may not be fully understood for years.

For civil rights advocates, the health crisis in Flint smacks of what has become known as environmental racism. Coined in the 1980s, the term refers to the disproportionate exposure of blacks to polluted air, water and soil. It is considered the result of poverty and segregation that has relegated many blacks and other racial minorities to some of the most industrialized or dilapidated environments.

via www.nytimes.com

Legitimate concerns about water quality appear to have been ignored in Flint. I can think of two reasons other than race why they may be. First, political ideology. Environmental issues don't rise to the top of the agendas of Republican Governors. Second, Flint is a depressed city and it is relatively easy to ignore the concerns of relatively poor voters. I might be wrong but I don't think this is an environmental Kuznets issue since drinking water quality is one of the more basic environmental goods in the U.S. (although I think there are drinking water quality environmental Kuznets curve studies in developing countries).

I did a Google Scholar search of environmental+racism+economics from 2011 to 2015 and didn't find much. On the third page of search results is this paper:

Wu, Xiaoyu, and Bowman Cutter. "Who votes for public environmental goods in California?: Evidence from a spatial analysis of voting for environmental ballot measures." Ecological Economics 70, no. 3 (2011): 554-563.

Abstract. Voting referenda provide direct evidence of the demand for public goods. A number of previous studies have used referenda to analyze the support for public environmental goods. These studies have used aggregate data from large jurisdictional units (usually counties) and summary income measures such as the mean or median, and have usually found that higher income areas offer greater support for environmental propositions. We examine environmental referenda voting in California using census block group data, spatial dependence controls, and detailed income distribution data. We find that household income has a negative marginal effect on environmental referenda voting for most of the income range when using census block data. In addition, controls for spatial dependence significantly reduce the magnitude of most coefficients. This suggests that OLS estimates of referenda determinants are biased. We also show that county level data may be subject to severe aggregation bias and might not be appropriate for referenda studies.

There is no mention of race (I'm sure it was a control variable but I'm stuck at home with Jonas so I can't get the entire paper from my library) and none of the 21 papers that cite this one appears to be a environmental racism papers.

Clever, this completely solves the free labor for for-profit journals problem