An introductory post/rant from a new contributor

To all of you in the Common Tragedies community I would like to introduce one more tragedy: my presence here as a contributor. My name is Tim Kidman, and I currently work at the Climate Action Reserve – an organization whose views and ideas I make no claim to represent in my posts here. What you read here are my thoughts alone, and do not represent my employer. This post has nothing at all to do with the Reserve. Of course, the Reserve has some pretty great ideas too, so I’d encourage everyone to check out our program. But you know the deal, so the above is my disclaimer.

When my friend Danny asked me to join the blog I immediately jumped at the opportunity. I am constantly intrigued by the power of economic theory to explain behavior and environmental issues, but I am far more amazed by the ingenuity and creativity of thinkers to employ these same theories to develop innovative remedies and policy mechanisms for the very same problems.

Now, none of what follows is to say that environmental economics is not one of the most valuable and powerful tools we have available to us today. I truly believe it is. What follows is simply to say that we need to always ask the question: are we trying to fit a square problem into a round solution? Because the truth is, no matter how many problems are round, a few are definitely square.

So, in my introductory post/rant I would like to echo a cautionary sentiment about market mechanisms, or at least our relationship with them, that Danny posted yesterday: “They are not a panacea, however, so don’t be afraid to be a little skeptical now and then.” The neatness and coherence of viewing environmental issues through a purely economic lens has immense intellectual appeal. It is scientific, structured, and at first glance offers a utopian vision of freedom, choice, environmental integrity, and that ever-present word – efficiency.

In grad school I definitely fell into this optimistic, almost religious faith in the ability of economics to explain and fix environmental problems as disparate as over-fishing and climate change – two that I actually believe it is particularly well suited for. In that context, I believe it served me and my classmates well; it allowed us to dig deeper, learn more, and think creatively without the constraints of the “real world.” But now that I am back in the real world, that optimism has been tempered. I still have faith in market mechanisms and am a strong believer in a cap-and-trade system. However, and this brings me back to Danny’s point, I think it’s important to recognize that certain issues, or certain aspects of issues, simply do not lend themselves to market mechanisms.

At a carbon conference a few weeks ago, Carl Pope of the Sierra Club stood up in front of a room of 850 attendees, and told us all that markets were not the only answer. It was, in my opinion, the best speech of the event. This was a room full of offset project developers, brokers, investors, regulators, and non-profits, all of whom had a vested interest in seeing the carbon market mature (myself included). It took courage to stand up as a keynote speaker and speak openly to this audience, but more than that it highlighted a paradigm shift that I think has gone largely unnoticed.

For decades, the environmental community and certain groups in particular have had the reputation of being idealistic, stubborn, and unwilling to place pragmatism over righteousness. In contrast, industry and investor advocates have cautioned against unbridled idealism and regulation– often very transparently – arguing instead that a pragmatic approach which balances societal and environmental goals should be considered. (I by no means condone the industry lobbying that has taken place, or the often disingenuous opinions proffered, but instead use this only to illustrate a point)

So back to Pope. The new environmental community, the one founded on economic theory, market mechanisms, pragmatism, and which has aligned itself much more closely with industry, risks falling into the same traps as past movements if we are not vigilant. Unbridled optimism and commitment to a single framework is risky. Whether that is a call for regulation, boycotts and protests, or a call for markets, no single framework provides all the answers. It may provide many, and I’d say most, but not all. Interestingly, it was an outspoken advocate of more traditional conservation that most recently brought this consideration into relief, grounding all of us and reminding us that results may be more important than the means.

Markets are great, and economics is a useful lens. But in the name of our ultimate goals, pragmatism and reality require that we recognize certain limitations. There are some issues that markets are simply ill-suited to solve. True, markets may not work because of imperfect information, too many players, etc. (not the theory itself, but rather its implementation), but if we really want to get something done then it is important to recognize that a different mechanism may be more appropriate. Skepticism of market mechanisms is important not just when considering the details of their implementation, but I would argue, even at the level of assessing their very appropriateness.

I recognize that this is a bit of a downer, especially for a first post, but I don’t intend it that way. There are more problems that can be and should be solved by employing and considering environmental economics than I can list. And because of that, it is important that as practitioners we focus our energy on those.

Good resources for econometrics

If that’s not an eye-catching title, I don’t know what is.

As a development economics and politics blogger, Chris Blattman stands out.  However, since adding him to my Google Reader feeds, I’ve noticed that he often links to extremely useful microeconometrics tutorials and guides.  The latest is a guide to regression discontinuity methods in economics, but previous links include IV vs. structural models and Andrew Gelman’s review of a new inexpensive volume on labor econometrics.

Which brings me to Gelman’s blog on statistics in the social sciences.  Gelman is a real live statistician with a joint appointment in political science.  A lot of his posts are pointing out abuses of statistics or interesting statistical tidbits.  But his biggest value added is that he looks as social science methods through the lens of a statistician.

Exogenous legal institutions as a channel to growth?

Most of the empirical literature on the relationship between legal institutions and growth employs cross-country data, but these data may be of limited use in learning how specific rules and laws affect specific paths of development. This paper exploits policy-induced variation in legal institutions across American Indian reservations to study a specific casual mechanism from institutions to growth. The variation is due to federal legislation, implemented during the 1950s and 1960s, that forced some American Indian tribes to transfer judicial jurisdiction to state courts while other tribes retained jurisdiction. The evidence indicates that American Indians on reservations under state jurisdiction were less likely to be denied home loans during the recent housing boom and the results are robust to corrections for potential selection bias. Other findings indicate that lender market concentration is lower on reservations under state courts, and that more per-capita housing credit has flowed to these reservations in recent years. These findings are consistent with a theory developed here that lenders are more uncertain about creditor rights under tribal law.

That is Dominic Parker on the effect that (exogenous changes in) legal institutions have had on access to credit on American Indian reservations.  The basic idea is that the U.S. imposed a known (i.e., less uncertain) legal framework on some reservations, and the reduction in uncertainty about creditor rights increased credit flows in these places.

The natural extension is to ask whether small countries with bad institutions can get an instant boost if they import the external legal instutions of mature, stable countries.  But it is not clear to me that uncertainty about creditor rights is the relevant constraint in most countries with bad institutions.  Rather, it seems to me that bias and corruption are more likely channels for hindering credit flows.  (In fact Parker is explicit in his paper that he is looking at the reduction in uncertainty about creditor rights, not the removal of bias and corruption, as the channel for improving credit access.)  Can Haiti import not only Great Britain’s legal institutions but also its enforcement capabilities?  The new and exogenous legal institutions have to be credible, and this seems like a much different challenge in many developing countries compared to American Indian reservations.

Here is Parker on whether federal land programs crowd out private conservation.

Deirdre McCloskey in DC

From the inbox:

Upcoming Luncheon of the Society of Government Economists

Thursday, December 4, 2008 (12:00 – 1:30 PM)

Deirdre McCloskey

“Statistical Significance is Essentially Meaningless (at the 5% Level).”

Deirdre McCloskey and Stephen Ziliak have been arguing for some years that accepted practices in econometrics are mistaken. In their current book, The Cult of Statistical Significance: How the Standard Error Costs Jobs, Justice, and Livesthey present the full case, drawing on examples from medicine, psychology, and economics. They note that the very simple point is not original.It has been made repeatedly by hundreds of the best statistical theorists and practitioners (Savage, Friedman, Zellner, Kruskal, Heckman) over the past century. And yet users of statistics continue to kill patients and misadvise governments. Why?

Chinatown Garden Restaurant, 618 H St., NW, Washington, DC
(For reservations, contact Steven.Payson@bea.gov)

Reservations are $16 for members, $20 for non-members.

You might recall that I posted on this thread of McCloskey’s research a few months ago.  Just in case you want to relive my econometric frustrations prior to the lunch.

Overheard

I have been far too distracted by global financial calamity this week to spend much time thinking or writing about environmental economics.  As way of apology I’ve rounded up some good quotes that touch on a few of the (often tenuous) links between the complete meltdown of our economic system and environmental economics:

But the gloomy investment climate suggests that clean energy needs a quick rebound in credit markets more than it needs lawmakers to renew tax credits if it is to avoid a desert of financing options for the rest of 2008 and 2009.

– Nathanial Gronewold and Michael Burnham at Greenwire

The most passable carbon pricing policy up until now was believed to be cap and trade, a system in which emission permits are bought, sold, banked, and so on. Will that begin to look undesirable in the wake of this crisis?

– Free Exchange, wondering whether market skepticism will influence the course of climate policy

I think we’ll only get one shot to set things right by throwing a ton of money at the problem, so we’d better think carefully before we throw it at symptoms rather than causes. … As far as the money is concerned, throw it at infrastructure. Increase worker bargaining power by offering Federally funded retraining sabbaticals for any worker over thirty who decides they want to retool. I’d rather see a new WPA than a new RTC.

–  Steve Randy Waldman, with smart thoughts on bailout money and infrastructure spending.  I do worry a bit though about where the green collar job nuts supporters are going to run with this.

Certainly AIG though with the construction bonds that they’re holding and with the insurance that they are holding very, very impactful to Americans…

– Sarah Palin, who caught a lot of flak — much of it deserved — for her responses to questions about the crisis.  However, she actually seems to be right about the construction bond point, as Tyler Cowen points out.  Credit conditions are going to make it difficult for municipalities to build infrastructure over the next couple years so in my mind this strengthens Waldman’s argument above.

It’s useful that Paulson seems to have excellent contacts and working relationships with China, which will be America’s most challenging bilateral relationship for the next decades.

– Jonathan Zasloff, arguing that Paulson should be kept on as treasury secretary regardless of who the next president is.  Zasloff previously discusses climate change — Paulson is a committed environmentalist — although he doesn’t draw the obvious link: doing anything substantial about climate means getting China on board, and ASAP.  China’s relationship with the U.S. right now is the most important bilateral relationship affecting global economic health, and long-term China’s relationship with the U.S. is the most important bilateral relationship affecting global environmental health.  Speaking of which, who is bailing out whom again?

surely it would be more rational for the Chinese to own the American financial system itself

– Robert Preston

In our central scenario, we estimate that the crisis could lower real GDP growth in 2008 and 2009 by an average of 1.8 percentage points per year.

– Jan Hatzius.  I’ll note that substantially reducing economic growth should also slow down carbon emissions in America over the next couple years!  Unfortunately this is rather like getting killed by lightning while looking for that silver lining in the cloud.  Speaking of which:

Annoying our neighbors so much that they cut off our oil supplies would, I suppose, be one way of helping us achieve energy independence, but it doesn’t seem like a particularly good idea.

– Hilzoy, wondering who exactly is the most knowledgeable person in the U.S. on energy issues these days.  (Hint: It’s not Sarah Palin.)

Significance

Evan’s post last week about statistical versus economic (I might call it actual) significance reminded me a recent set of articles in Wired magazine that announced “the end of theory“.  The article offered an interesting (if florid) take on “the Petabyte Age”, where huge volumes of raw data make the scientific method obsolete:

At the petabyte scale, information is not a matter of simple three- and four-dimensional taxonomy and order but of dimensionally agnostic statistics. It calls for an entirely different approach, one that requires us to lose the tether of data as something that can be visualized in its totality. It forces us to view data mathematically first and establish a context for it later. For instance, Google conquered the advertising world with nothing more than applied mathematics. It didn’t pretend to know anything about the culture and conventions of advertising — it just assumed that better data, with better analytical tools, would win the day. And Google was right. …

Scientists are trained to recognize that correlation is not causation, that no conclusions should be drawn simply on the basis of correlation between X and Y (it could just be a coincidence). Instead, you must understand the underlying mechanisms that connect the two. Once you have a model, you can connect the data sets with confidence. Data without a model is just noise.

But faced with massive data, this approach to science — hypothesize, model, test — is becoming obsolete. …

There is now a better way. Petabytes allow us to say: “Correlation is enough.” We can stop looking for models. We can analyze the data without hypotheses about what it might show. We can throw the numbers into the biggest computing clusters the world has ever seen and let statistical algorithms find patterns where science cannot.

As you can probably imagine, this prompted some discussion both among natural and social scientists.  (Both those links are worth reading, as are the posts they link to.)  My only specific comment on the Wired articles is that while they do a nice job exploring some fields where data is proving very powerful, the implicit premise seems to be that causation is dead — long live correlation! — and this is oversold.

The link to Evan’s post that I wanted to highlight was that it is exactly this abundance of data (combined with the powerful statistical tools we have to analyze it) that have made for such an intense focus on statistical significance in recent years.  This is completely appropriate when you are just mucking around in data looking for correlations.  After all, if I look at a set of 20 random variables (that have no relationship in reality) then on average I should should find one relationship between them that is significant at the p=0.05 level.  Remember, finding statistically significant correlation at p=0.05 means that there is a 1 in 20 chance that the correlation is just a random artifact.  Well, when you start piling through hundreds (or thousands) of variables and trying out scores (or hundreds) of specifications, random artifacts start showing up! (Everyone who actually understands statistics is now allowed to bang their head against their desk and correct my horribly sloppy and imprecise language in the comments.)

And this gets back to that difference that Evan was talking about.  When you are doing reduced form econometrics and not pretending you have any idea what the relationship between any of your variables should be, you had better show some pretty impressive t stats — and start coming up with a reasonably good post hoc argument — before I pay much attention to your paper.

On the other hand, if theory tells us that there is good reason to suspect that a set of variables share a causal relationship — if we have a model that we think represents reality — then it is quite a different thing to find that there is a 1 in 20 (or even 1 in 10!) chance that our correlation is just random.  When data analysis backs up a theoretical prediction — even with only 90 (or even 80!) percent confidence — then our belief in the theory should get stronger.

The upshot is that I’m not ready to declare that correlation is enough.  Data is great, but it’s a lot better when you combine it with some theory.

Chasing that **

I do a lot of econometric work, and I rarely post on it.  This is mostly for the benefit of the audience, who may not want to read about the Bayes Information Criterion or Maddala and Wu’s panel unit root test.

Inspired by a post at EnvEcon about Dierdre (then Donald) McCloskey’s take on the academic life, I wanted to post a paper from the Journal of Economic Literature that is generally applicable to applied econometrics in all subfields.  I’ve found McCloskey an interesting figure, not just because she was, until 1995, “Donald”.  Her pursuits are wide-ranging and interdisciplinary, covering history, economics, feminism, law, rhetoric, and philosophy.  From McCloskey’s bio:

I describe myself as a postmodern free-market quantitative rhetorical Episcopalian feminist Aristotelian woman who was once a man.

I first discovered McCloskey while reading David Colander’s The Changing Face of Economics, which is a fascinating series of interviews with unorthodox (I wouldn’t necessarily say that all were heterodox) economists, such as ecological economists and behavioralists.  I took notice of McCloskey partly due to her interesting work and partly because she used to teach at my alma mater, the University of Iowa (for which she had some harsh words).  Anyway, one of her major points on the rhetoric of economics is covered in her JEL article from 1996 with Stephen Ziliak entitled, “The Standard Error of Regressions.”:

That is, most beginning econometrics books even now, unlike DeGroot and Goldberger and before them the modern masters of statistics, do not contrast economic and statistical significance…

…The student from the outset of her statistical education, therefore, is led to believe that economic (or suvstantive) significance and statistical significance are the same thing.  Hoel explains: “This word ['not significant'] arises from the face that such a sample value is not compatible with the hypothesis and therefore signifies that some other hypothesis is necessary”  The elementary point that “there is no sharp border between ‘significant’ and insignificant’, only increasingly strong evidence as the p-value decreases” is not found in most of the earlier books from which most economists learned statistics and econometrics.

McCloskey and Ziliak go on to survey papers from AER to see whether these issues of interpretation show up in practice.  An appalling proportion of papers gravely misused the concepts of significance in some way; thankfully, the problem seemed to be diminishing: misuse was decreasing in PhD vintage and the sample is from the 1980s.

I find myself in a lucky situation where economic significance is generally emphasized, as policy-related applied work must be set in context.  However, there are times when I feel like I’m chasing that elusive asterisk for p < 0.05 (or even better, two of ‘em for p < 0.01).  I was recently at a presentation where a scholar gave a result with p = 0.06, and he played it down as considerably less significant than a coefficient with p < 0.05.  I’ve also had the experience of presenting economically meaningful coefficients significant at 0.05 < p < 0.10 and being told that they might be worthwhile but in effect were ignored.  I realize that confidence is important, but I feel a type I error happening with a 1 in 10 probability is not a reason to ignore economically signficant coefficients.  When you think about the somewhat arbitrary nature of what passes for statistically significant and what does not (and, thus, seriously influences what gets published and what does not), it kind of puts a damper on the whole “applied economics is a science” thing.  Not to underplay the extremely useful nature of these methods, but I’m once again reminded that metrics has an artistic flourish to it.

The curse is broken?

Why hasn’t this been reported more widely?

The correlation between resource dependence and slow growth and conflict, therefore, does not imply causation from the former to the latter. Instead, causality appears to be running from weak institutions and conflict to resource extraction as the default sector, which produces resource dependence as the final outcome. Resource dependence appears as a symptom, rather than a cause of underdevelopment.

The authors argue that previous research on the ‘natural resource curse’ has been unable to correctly identify which way causality runs:

The standard resource variable used by Sachs and Warner, as well as by Collier and Hoeffler, is primary exports divided by a measure of national income. It thus captures the resource dependence of economies, rather than abundance. A negative correlation between this variable and growth could mean that resources lead to slower economic growth, as suggested by the curse proponents. Alternatively, it could mean that poor economic development policies–leading an economy to become dependent on its primary exports–dampen growth. Similarly, although a negative correlation between the resource variable and institutional quality may imply that resources undermine institutions, it might also capture that the resource sector is the “default sector” in the absence of decent institutions when nobody is willing to invest in alternative forms of capital. Finally, a positive correlation between the resource variable and conflict may indeed mean that resources trigger conflict. But it may also be the case that conflict makes countries dependent on resource extraction–the default activity that still takes place after other economic sectors (more mobile or, perhaps, better linked to the rest of the economy) have come to a stop. If so, resources are not a curse to development, but rather a safety net to support people and economies even under adverse circumstances.

They argue that economic dependence on natural resources is endogenous in exactly this way and use data on resource endowment — rather than resource exports — as an explanatory variable for economic growth.

When using the new World Bank variable to proxy for resource abundance, we find that the direct effect of resource wealth (particularly the subset of mineral resource wealth) on income growth is positive and significant. All things considered, an increase in subsoil wealth by one standard deviation–roughly the difference between Senegal and Sweden–would have brought Senegal’s growth performance on a par with that of Mozambique or Kenya; not a huge improvement, but certainly not a growth curse.

Similarly, resource wealth also attenuated the risk of conflict. This is due to a positive indirect effect: Resource wealth raises income, and higher incomes, in turn, reduce the risk of conflict. Again, although the aggregate impact of resource abundance is slight–amounting to less than a 5% reduction in the risk of war in case of a standard-deviation increase in resources–it is still statistically significant.

If this result holds up it will be a significant finding in development economics and could overturn almost 2 decades of conventional wisdom on the curse of natural resources.  The full text is here (gated); a non-gated version is here.

Assorted links

1. Agricultural Subsidies: Still a Bad Idea. Felix Salmon explains why removing ag subsides and taxing carbon are similar, and why they both make sense. Free Exchange squares the circle with a discussion of biofuels.

2. Who Pays a Tax? Tim Haab’s two-part series is here and here.

3. 6 Cities That Were Caught Shortening Yellow Light Times For Profit. What happens when your city stands to make money off of lawbreaking? Yep, that’s right, they make it harder to avoid breaking the law.

4. Malaria and the politics of disease. Efforts to fight malaria seem to be ramping up quickly. But even if near-term success can be achieved, will many be left worse off in the long run?

5. Congestion pricing works. Evidence from California.

6. Location, location, location. The premium for urban living.

7. The cost of siting transmission lines.  This came up yesterday in the seminar on curbing electricity demand at RFF as one of the key uncertainties in the future of electricity, given the political or economic forces that will bring new types of resources onto the grid in the coming years.  (Video from the event should be up in the next few days.)

More thoughts from the Weitzman event

That Marty Weitzman talk I attended last month not only featured Weitzman but also had some pretty amazing discussants including Richard Posner and Tom Schelling. There were plenty of juicy tidbits (besides the one I’ve already mentioned) and so I’ve gone through my notes and have tried to pull out a few of the highlights. There’s enough here that I’m putting it all below the fold…

Words of truth and challenge

In my view anyone doing policy economics has an obligation to learn more about ethics — much more — than the guy in the street would know. Would someone doing experimental economics feel free of the obligation to learn some empirical psychology? Would someone doing trade feel free of the obligation to learn some trade law, some history, and some political science? No. What’s the difference? Economists like to separate the “positive” and “normative” aspects of what they do, but this distinction has not much impressed the moral philosophers who have looked at it nor has it impressed Amartya Sen. The very decision to use economic tools emphasizes some considerations and excludes others. The final policy analysis is not just pure prediction but rather it is also an implicit presentation and weighting of both different kinds of information and different values. So if you are doing policy economics, it is imperative that you think about ethics at a very deep level, and read widely in ethics. You are doing ethics whether you like it or not!

That is Tyler Cowen. He is writing in the specific context of trade — rather than environmental — economics, but the point is a broad one. (Indeed, he links trade to environmental economics in his closing paragraph.) I was struck forcefully by his words. It’s easy to fall into the trap of explaining what the most “efficient” policy is, and then try to duck the distributional questions. Or assume that they are left to be negotiated by “society.” More fundamentally, as Cowen points out, using economics as a tool of policy analysis means weighting values and considerations, thus implying a set of ethical choices.

I feel challenged to try to more explicitly identify the weighted “priors” I bring to any question in environmental economics. This doesn’t mean I’m throwing out benefit-cost analysis as a tool for making policy decisions, but it does mean I’ll try to identify more clearly what influence those priors have on my analysis and be more open to discuss how alternative values could result in different conclusions.

I also started thinking about what texts would be considered required reading in ethics for economists (and perhaps particularly environmental economists). Here is a list from Cowen. Do readers have any suggestions of their own?

Can’t we all just get along?

Over on Gristmill Michael Tobis wonders, “should economists rule?”. While that’s clearly a loaded question, his real argument, that we should stop pursuing economic growth, is much more interesting. I’m going to post my response to this on Gristmill as well as below. Looking at the comments on there so far, I realize that this post could provoke a fair amount of rage and anger from those, like Tobis, who are disturbed by a perceived monopolization over public debate by economists of late. Though it probably won’t matter, I’d like to point out that I certainly don’t believe that we should be categorically opposed to non-economic arguments. In fact interdisciplinary dialogue is imperative for solving most policy questions, including those related to the environment. At the end of the day, what matters is how good our answers are, not how we got there. Hopefully we can use this space to narrow some of the economist/ non-economist divide. With that being said, here we go.