Children and Climate Change: The Science of Climate Change

Michael Oppenheimer and I have an  overview of the science of climate change in the new Children and Climate Change issue of the open-access Princeton-Brookings journal The Future of Children  :

A defining theme of this article is the need to balance high uncertainty in some areas with relative certainty in others. As we will show, we now have overwhelming evidence that human emission of greenhouse gases has already begun to change the climate and that it will continue to do so unless emissions are halted; hence we call this climate change anthropogenic, from the Greek for human influenced. Moreover, ample evidence indicates that we can expect many changes in the weather and the climate that will fall outside the range of human experience. Unless we reduce emissions drastically, those changes are expected to have pervasive impacts worldwide, including, in some cases, the destabilization or destruction of ecological and social systems. Thus the costs of inaction are high. At the same time, enormous uncertainty surrounds any forecast of specific outcomes of climate change. Which regions will be affected and in what ways, how quickly changes will occur, and how humans will respond are all impossible to know with certainty, given the complex natural and social forces involved. From a risk management perspective, the possibility of extremely negative outcomes means climate change has much in common with other large-scale global threats such as conflict between nuclear powers, wherein the potential for highly undesirable and irreversible outcomes is real but very difficult to predict with precision. 

We tried to provide an overview of the physical science of climate change suitable for non-specialists and policymakers concerned with children's well being, in particular highlighting what we can expect to be major impacts on children's livelihoods given the current state of the climatological and empirical climate impacts literatures. The rest of the issue contains overviews of multiple other aspects of climate change relevant to policymakers, from what we know about the likely excess temperature effects on health to mobilizing political action on behalf of future generations. You can check it out here.

The potential for global fisheries management

Status and Solutions for the World’s Unassessed Fisheries
Christopher Costello, Daniel Ovando, Ray Hilborn, Steven D. Gaines, Olivier Deschenes, Sarah E. Lester

Recent reports suggest that many well-assessed fisheries in developed countries are moving toward sustainability. We examined whether the same conclusion holds for fisheries lacking formal assessment, which comprise >80% of global catch. We developed a method using species’ life-history, catch, and fishery development data to estimate the status of thousands of unassessed fisheries worldwide. We found that small unassessed fisheries are in substantially worse condition than assessed fisheries, but that large unassessed fisheries may be performing nearly as well as their assessed counterparts. Both small and large stocks, however, continue to decline; 64% of unassessed stocks could provide increased sustainable harvest if rebuilt. Our results suggest that global fishery recovery would simultaneously create increases in abundance (56%) and fishery yields (8 to 40%).

"Climate Extremes: Recent Trends with Implications for National Security"

Last year, I participated in a few workshops where we thought through some of the "worst case scenarios" as well as potential mechanisms through which climatic changes could influence US national security interests in the next decade or so. The report (which I did not write) is now out (here, article here). The authors are trained geoscientists, so the text is focused on the physics side of the problem.

CLIMATE EXTREMES AND NATIONAL SECURITY – THE BOTTOM LINE
Climate change has entered the mainstream as a potential threat to U.S. national security. The 2010 Quadrennial Defense Review, and the 2010 National Security Strategy all identify climate change as likely to trigger outcomes that will threaten U.S. security. These assessments have had to rely on projections of climate change tuned to identify impacts over roughly a one-century time frame. This time frame is driven by the nature of the questions that dominated the initial literature (e.g., what impacts can  be expected from a doubling of pre-industrial carbon dioxide) and the fact that global climate models are generally able to resolve expected impacts only over large scales and the long term.Having arrived at a condition where climate change has been identified as a likely threat to U.S. national security interests, but with little ability to clarify the nature of expected climate impacts over a timeframe that is relevant to security decision-makers, the authors decided to focus on the near-term impacts from climate change (over the next decade). In short, the analysis finds that, absent unknown or unpredictable forces, the increase in extreme events observed in the past decade is likely to continue in the near term as accelerated warming and natural variability combine to produce changing weather conditions around the world. This will impact Water Security, Energy Security, Food Security, and Critical Infrastructure, and brings into focus the need to consider the accelerating nature of climate stress, in concert with the more traditional political, economic, and social indicators.

Some people may think the headlines from the report sound alarmist (I'm just guessing), but I think the text is quite pragmatic:

What was once a 1 in 100 year anomaly is likely to become a 1 in 10 or 1 in 30 year anomaly or even more frequent in the near future. Our infrastructure and agriculture is not designed to accommodate  the increasing frequency and prevalence of such extremes. Human security and the interests of most nations are at stake as a result of such increasing  climate stress. The national security context will change. The potential for profound impacts upon water, food and energy security, critical infrastructure, and ecosystem resources will  influence the individual and collective responses of nations coping with climate changes. U.S. national security interests have always been influenced by extreme weather patterns. Now the risks will become larger and more apparent. The study renders the judgment that the increasingly disruptive influences of climate extremes necessitate their careful consideration in threat analysis, mitigation, and response. It is in the best interest of the U.S. to be vigilant about extreme weather patterns, the behavior of nations in their attempts to mitigate or adapt to the effects of changing extremes, and impacts on social, economic, and political well-being.

h/t Center for Climate and Security

Building Back Worse

It's sometimes hypothesized that after a natural disaster, populations "Build Back Better," meaning that the reconstruction of damaged infrastructure leaves the population better off after the disaster than they were before it struck.  There is some economic logic to this hypothesis, since we know that populations don't always update expensive capital investments when they should.  If capital is outdated, then an exogenous shock that motivates the population to replace the old capital with new capital might end up increasing the economy's output in the long-run.  Many of us are familiar with the example of a cell phone that is old and frustrating, but we don't feel like its worth replacing until we drop it in the pool by mistake -- and then find that upgrading to the latest smartphone makes us much more productive.

Politicians in the US, it seems, are required to tell the local population that they will "build back better" after a disaster strikes.  After Hurricane Sandy, we heard a lot of leaders talking about how the tri-state area will be stronger and better than it ever was, probably in part because nobody wants to hear otherwise and in part because this kind of logic is important for obtaining reconstruction funding.  Healy and Malhotra have a nice article (here) demonstrating that obtaining this kind of funding for reconstruction is important for an incumbent's re-election (recall: Hurricane Sandy -> Federal relief funding -> NJ Governor Christie's endorsement -> Obama earns votes in reelection).  And just before the Superbowl, I saw this video about the Superdome's post-Katrina reconstruction, which is not shy about endorsing the BBB hypothesis.

Importantly, though, the BBB hypothesis still remains a hypothesis, and there is no robust empirical evidence that populations actually do build back better, in aggregate, after catastrophic events. To remind that we shouldn't take the anecdotes and political rhetoric above too seriously, Amir Jina points us to an interesting IRIN report that suggests populations affected by Typhoon Bopha are building back worse:

Ethos or Logos?

Last week, Bjorn Lomborg wrote an opinion piece in the WSJ preemptively attacking the climate policies that he speculates Obama will endorse. I don't usually read this kind of thing, but journalists at Climate Science Watch asked what I thought about it since they didn't believe it.  Reading the article, I was surprised the WSJ had published it -- not because the citations were not 100% correct, but because the overall logic of the essay was flawed in obvious ways (regardless of your political stance). This is the kind of thing a copy-editor should have picked up on.  My reply to the CSW mainly focused on this central logical flaw.

Interestingly, when CSW posted its reply to BL (here), which drew on many scientific experts, it was focused entirely on discrediting individual statements that BL had made

Displaying his trademark doublethink, Bjorn Lomborg’s latest op-ed in the Wall Street Journal switches between recognizing the risks of climate change and rejecting the need for meaningful action in the near term. Lomborg incorporates misleading and discredited scientific information to justify dangerous delays in climate action. 

rather than pointing out that the giant "if-then" statement at the core of the article's architecture would obviously return an error if it were fed into any computer capable of boolean logic.

This struck me because the dialogue (in both directions) was focused on discrediting one's opponent, by demonstrating they don't understand science (BL does this to Obama, and CSW does this to BL), rather than finding a logical solution to the problem (or simply having a logical discussion about it).  This seemed unfortunate, since in this particular case, the physical science is pretty irrelevant to the actual policy discussion. The entire discussion should be focused on the economics. I blame BL for inappropriately bringing the science into the discussion, but I wish CSW had pointed out that that was the error, since I think doing so (here and elsewhere) would get us back on track to meaningful discussion rather than escalating the scientific mudslinging.

My fully reply to the CSW is below the fold (I had been in referee-mode at the time, which is probably evident).

Is it true that "Everyone's a winner?" Dams in China and the challenge of balancing equity and efficiency during rapid industrialization

Jesse and I both come from the Sustainable Development PhD Program at Columbia which has once again turned out a remarkable crop of job market candidates (see outcomes from 2012 and 2011). We both agreed that their job market papers were so innovative, diverse, rigorous and important that we wanted to feature them at FE.  Their results are striking and deserve dissemination (we would probably post them anyway even if the authors weren't on the market), but they also clearly illustrate what the what the Columbia program is all about. (Apply to it here, hire one of these candidates here.) Here is the third and final post.

Large infrastructure investments are important for large-scale industrialization and economic development. Investments in power plants, roads, bridges and telecommunications, among others, provide important returns to society and are compliments to many types of private investment. But during rapid industrialization, as leaders focus on growth, there is often concern that questions of equity are cast aside. In the case of large-scale infrastructure investments, there are frequently populations ("losers") that suffer private costs when certain types of infrastructure are built -- for example, people whose homes are in the path of a new highway or who are affected by pollution from a power plant.

In public policy analysis and economics, we try to think objectively of the overall benefits of large investments to an entire society, keeping in mind that there will usually be some "losers" from the new policy in addition to a (hopefully larger) group of "winners."  In the cost-benefit analysis of large projects, we usually say if that a project is worth doing if the gains to the winners outweigh the loses to the losers -- making the implicit assumption that somehow the winners can compensate the losers for their loses and continue to benefit themselves. In cases where the winners compensate the losers enough that their losses are fully offset (i.e. they are no longer net losers), we say that the investment is "Pareto improving" because nobody is made worse off by the project.

A Pareto improving project is probably a good thing to do, since nobody is hurt and probably many people benefit. However, in the case of large infrastructure investments, it is almost guaranteed that some groups will be worse off because of the project's effects, so making sure that everyone benefits from these projects will require that the winners actually compensate the losers. Occasionally this occurs privately, but that tends to be uncommon, so with large-scale projects we often think that a central government authority has a role to play in transferring some of benefits from the project away from the winners and towards the losers.

But do these transfers actually occur? In a smoothly functioning government, one would hope so.  But the governments of rapidly developing countries don't always have the most experienced regulators and often pathologies, like corruption, lead to doubt as to whether large financial transfers will be successful.  Empirically, we have little to no evidence as to whether governments in rapidly industrializing countries (1) accurately monitor the welfare lost by losers in the wake of large projects and (2) have the capacity necessary to compensate these losers for their loses. Thus, establishing whether governments can effectively compensate losers is important for understanding whether large-scale infrastructure investments can be made beneficial (or at least "not harmful") for all members of society.

Xiaojia Bao investigates this question for the famous and controversial example of dams in China. Over the last few decades, a large number of hydroelectric dams have been build throughout China. These dams are an important source of power for China's rapidly growing economy, but they also can lead to inundation upstream, a reduction in water supply downstream, and a slowed flow of water that leads to an accumulation of pollutants both upstream and downstream.

Bao asks whether the individuals who are adversely affected by new dams are compensated for their losses. To do this, she obtains data on dams and municipal-level data on revenue and transfers from the central government.   She uses geospatial analysis to figure out which municipalities are along rivers that are dammed and also which are upstream, downstream or at the dam site.  She then compares how the construction of a new dam alters the distribution of revenues and federal transfers to municipalities along the dammed river, in comparison to adjacent municipalities that are not on the river.

Bao finds that the Chinese government has been remarkably good at compensating those communities who suffer when dams are built.  Municipalities upstream of a dam lose the most revenue both while the dam is being built and after it become operational. But at the same time, the central government increases transfers to those municipalities sufficiently so that these municipalities suffer no net loss in revenue. In contrast, populations just downstream look like they benefit slightly from the dam's operation, increasing their revenue -- and it appears that the central government is also good at reducing transfers to those municipalities so that these gains are effectively "taxed away." The only group that is a clear net winner are the municipalities that host the actual dam itself, as their revenue rises and the central government provides them with additional transfers during a dam's construction.

These findings are important because we often worry that large-scale investment projects may exacerbate existing patterns of inequality, as populations that are already marginalized are saddled with new burdens for the sake of the "greater good." However, in cases where governments can effectively distribute the benefits from large projects so that no group is made worse off, then we should not let this fear prevent us from making the socially-beneficial investments in infrastructure that are essential to long run economic development.

The paper:

Dams and Intergovernmental Transfer: Are Dam Projects Pareto Improving in China?
Xiaojia Bao  

Abstract: Large-scale dams are controversial public infrastructure projects due to the unevenly distributed benefits and losses to local regions. The central government can make redistributive fiscal transfers to attenuate the impacts and reduce the inequality among local governments, but whether large-scale dam projects are Pareto improving is still a question. Using the geographic variation of dam impacts based on distances to the river and distances to dams, this paper adopts a difference-in-difference approach to estimate dam impacts at county level in China from 1996 to 2010. I find that a large-scale dam reduces local revenue in upstream counties significantly by 16%, while increasing local revenue by similar magnitude in dam-site counties. The negative revenue impacts in upstream counties are mitigated by intergovernmental transfers from the central government, with an increase rate around 13% during the dam construction and operation periods. No significant revenue and transfer impacts are found in downstream counties, except counties far downstream. These results suggest that dam-site counties benefit from dam projects the most, and intergovernmental transfers help to balance the negative impacts of dams in upstream counties correspondingly, making large-scale dam projects close to Pareto improving outcomes in China.

In figures...

In China, Bao obtains the location, height, and construction start/stop dates for all dams built before 2010.

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For every dam, Bao follows the corresponding river and calculates which municipalities are "upstream" and which are "downstream." She then computes finds comparison "control" municipalities that are adjacent to these "treatment" municipalities (to account for regional trends). Here is an example for a single dam:

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Bao estimates the average effect of dam construction (top) and operation(bottom) on municipal revenues as a function of distance upstream (left) or downstream (right).  Locations just upstream lose revenue, perhaps from losing land (inundation) or pollution. Locations at the dam gain revenue, perhaps because of spillovers from dam-related activity (eg. consumer spending). During operation, downstream locations benefit slightly, perhaps from flood control.

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Government transfers during construction/operation upstream/downstream. Upstream locations receive large positive transfers. Municipalities at the dam receive transfers during construction. Downstream locations lose some transfers (taxed away).

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Transfers (y-axis) vs. revenue (x-axis) for locations upstream/downstream and at the dam site, during dam construction. Locations are net "winners" if they are northeast of the grey triangle. Upstream municipalities are more than compensated for their lost revenue through transfers.   Municipalities at the dam site benefit through revenue increases and transfers.

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Same, but for dam operation (after construction is completed). Upstream locations are compensated for losses. Benefits to downstream locations are taxed away. Dam-site locations are net "winners".

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Were the cost estimates for Waxman-Markey overstated by 200-300%?

Jesse and I both come from the Sustainable Development PhD Program at Columbia, which has once again turned out a remarkable crop of job market candidates (see outcomes from 2012 and 2011). We both agreed that their job market papers were so innovative, diverse, rigorous and important that we wanted to feature them at FE.  Their results are striking and deserve dissemination (we would probably post them anyway even if the authors weren't on the market), but they also clearly illustrate what the what the Columbia program is all about. (Apply to it here, hire one of these candidates here.) This is the first post.

Good policy requires good cost-benefit analysis. But when we are developing innovative policies, like those used to curb greenhouse gas emissions, it's notoriously difficult to estimate both costs and benefits since no analogous policies have ever been implemented before.  The uncertainty associated with costs and benefits tends to make many forms of environmental policy difficult to implement in part because the imagined costs (when policy-makers are considering a policy) tend to exceed actual costs (what we observe after policies are actually implemented). Kyle Meng develops an innovative approach, linking Intrade predictions about the success of Waxman-Markey with stock-market returns and abrupt political events, to measure the cost of the bill to firms as predicted by the market. This is very different from standard technocratic approaches used by the government to assess the cost of future policies, which rely on parameterized models of technology and econometric models of behavior ("structural models").

By relying on the market, Meng infers what players in affected industries actually expect to happen in their own industry. The result is a bit surprising: Meng estimates that standard costs-estimates for WM (produced before it failed to pass) are 200-300% larger than what players in the industry actually expected it to cost them.  But this still didn't stop industry players from fighting the bill -- one of the ways that Meng validates his approach is to use lobby records to show that firms which expect to suffer more from the bill (as recovered using his approach) spend more money to fight it.

It's tough to tell whether Meng's approach or the structural models are more accurate predictors of firm-level costs since WM was never brought into law, so the outcomes will remain forever unobserved. But he does show that for several similar laws (eg. the Montreal Protocol), the structural predictions tended to overestimate the actual costs of implementation (which were observed after the law was implemented and outcomes observed) by roughly a factor of two. This doesn't prove that Meng's approach is more accurate, but it shows that his estimate for the bias of the structural approach (with regard to WM) is consistant with the historical biases of these models.

The paper:

The Cost of Potential Cap-and-Trade Policy: An Event Study using Prediction Markets and Lobbying Records
Kyle Meng

Abstract: Efforts to understand the cost of climate policy have been constrained by the limited number of policies available for evaluation. This paper develops an empirical method for forecasting the expected cost to firms of a proposed climate policy that was never realized. I combine prediction market prices, which reflect market beliefs over regulatory prospects, with stock returns in order to estimate the expected cost to firms of the Waxman-Markey cap-and-trade bill, had it been implemented. I find that Waxman-Markey would have reduced the market value of a listed firm by an average of 2.0%, resulting in a total cost of $165 billion for all listed firms. The strongest effects are found in sectors with greater carbon and energy intensity, import penetration, and exposure to U.S. product markets, and in sectors granted free allowances. Because the values of unlisted firms are not observed, I use firm-level lobbying expenditures within a partial identification framework to obtain bounds for the costs borne by unlisted firms. This procedure recovers a total cost to all firms between $110 and $260 billion. I conclude by comparing estimates from this method with Waxman-Markey forecasts by prevailing computable general equilibrium models of climate policy.

In figures...

Abrupt political events that affect the expected success of WM are quantified by looking at expectations in Intrade markets:

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When WM appears more likely, the stock prices of CO2 intensive firms falls on average:

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Firms that are more CO2 intensive are affected more strongly:

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Firms whose stock prices are more responsive to WM lobby harder against it:

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How these cost estimates compare with structural cost estimates, and similar statistics for historical regulations that actually passed into law.

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Take home summary: Cap and trade in the USA probably would have been cheaper to implement than we thought, according to the firms it was going to regulate. 

Setting technological goals for political feasibility in US climate legislation

In Nature Climate Change:

Willingness to pay and political support for a US national clean energy standard
Joseph E. Aldy, Matthew J. Kotchen & Anthony A. Leiserowitz

Abstract: In 2010 and 2011, Republicans and Democrats proposed mandating clean power generation in the electricity sector. To evaluate public support for a national clean energy standard (NCES), we conducted a nationally representative survey that included randomized treatments on the sources of eligible power generation and programme costs. We find that the average US citizen is willing to pay US$162 per year in higher electricity bills (95% confidence interval: US$128–260), representing a 13% increase, in support of a NCES that requires 80% clean energy by 2035. Support for a NCES is lower among non-whites, older individuals and Republicans. We also employ our statistical model, along with census data for each state and Congressional district, to simulate voting behaviour on a NCES by Members of Congress assuming they vote consistently with the preferences of their median voter. We estimate that Senate passage of a NCES would require an average household cost below US$59 per year, and House passage would require costs below US$48 per year. The results imply that an ‘80% by 2035’ NCES could pass both chambers of Congress if it increases electricity rates less than 5% on average.

AGU Science Policy Recap

Last week I had the pleasure of attending the first AGU Science Policy Conference in DC. One of the things I like the most about AGU events is the wide variety of academic fields from which attendees are drawn, and even given the comparatively narrow focus of this conference (there were only about twenty sessions, compared to the AGU annual meetings's thousands) the number of interesting ideas and novel concepts afloat was overwhelming. Below the fold are selected highlights, notes, and interesting errata from the two days I was there...

Model Uncertainty vs Policy Uncertainty in climate projections

Policy-makers often suggest that climate model projections remain too uncertain to base policy on, frequently blaming the quality of the modeling science.  But an under-appreciated fact is that policy-makers are themselves injecting roughly the same amount of uncertainty into the models. (Adam Sobel helped me fully appreciate this fact.)

The range of projections for a single emission scenario in 2100 (in the IPCC graph below, one of the grey bars on the right) is probably about 2.5 degrees Celsius.  But the range of average projections across the various emissions scenarios (compare the colored mean-stripe across the grey bars) is about 2 degrees Celsius.  The climate modelers are "responsible" for reducing the uncertainty of a projection for a given emissions scenario, but the policy-makers are "responsible" for determining which emissions scenario we're on.  Thus, about half of our uncertainty regarding climate-induced changes by 2100 is due to our inability to choose a policy trajectory. 

Figure SPM.5. Solid lines are multi-model global averages of surface warming (relative to 1980–1999) for the scenarios A2, A1B and B1, shown as continuations of the 20th century simulations. Shading denotes the ±1 standard deviation range of individual model annual averages. The orange line is for the experiment where concentrations were held constant at year 2000 values. The grey bars at right indicate the best estimate (solid line within each bar) and the likely range assessed for the six SRES marker scenarios. The assessment of the best estimate and likely ranges in the grey bars includes the AOGCMs in the left part of the figure, as well as results from a hierarchy of independent models and observational constraints.

Global Policy Journal

I ran across a copy of this new journal in the lounge of the WWS.  It looks pretty interesting and boasts an impressive editorial board.  I like their approach of focusing on global issues while pursuing interdisciplinary and applied authors/readers (and I don't know any other journals that have had a contribution by General David Petraeus).

Here's the editorial statement:

Global Policy has a multi-disciplinary, interdisciplinary and international outlook that is committed to developing the accuracy, forward lookingness and policy relevance of academic research. It will not privilege a particular ethnocentric approach but will reflect a multiplicity of approaches that are indicative of the emergence of a global system of multipolar governance and policymaking. 

The editors’ approach to selecting material will be:

Committed to advancing the academic study of global policy and the politics in which it is embedded; Open  to interdisciplinary and transdisciplinary contributions; Reflexive in its consideration of diverse political discourses on global problems; Engaged in respect of its contribution to public debate and understanding of urgent global policy issues and; Serious in its commitment to the publication of only world class academic scholarship and the work of key public and private figures or authorities. 

The scope of Global Policy’s content can be specified by a number of criteria:

• 01. Globally relevant risks and collective action problems.
• 02. Policy challenges with global impact.
• 03. Competing and converging discourses of global policy and governance.
• 04. Case studies of policy with clear lessons for other countries and regions.
• 05. The interrelationship between policy, politics and institutions at the global level, with implications for institutional design. 
• 06. Conceptual, theoretical and methodological innovations needed to explain and develop global policy.

The editors are committed to developing both the highest standards of scholarship and evidence based reasoning by authors, with the scholarly articles subject to rigorous peer review. We are at the same time committed to the effective communication of research in the most accessible and professional fashion. We shall use a set of editorial conventions that do not compromise on accuracy and the proper explanation of methods, but that do systematically prioritise readers’ interests in the excellent presentation of data and complete clarity of exposition. We shall seek to engage meaningfully with the widest range of readers and contributors

Ask an economist

Or, more aptly, ask 40 of the top economists alive what they think about a given policy statement. I found out about the IGM Forum's Economic Experts Panel from Luke Stein while at the American Economic Association meeting last weekend. The responses are best for yielding insight into what is and is not considered an open question from the point of view of economics. It's only been going on for a little while as far as the website seems to indicate, but the responses are illuminating. A sampling:

• "In general, using more congestion charges in crowded transportation networks — such as higher tolls during peak travel times in cities, and peak fees for airplane takeoff and landing slots — and using the proceeds to lower other taxes would make citizens on average better off."
• "A tax on the carbon content of fuels would be a less expensive way to reduce carbon-dioxide emissions than would a collection of policies such as “corporate average fuel economy” requirements for automobiles."
• "Public school students would receive a higher quality education if they all had the option of taking the government money (local, state, federal) currently being spent on their own education and turning that money into vouchers that they could use towards covering the costs of any private school or public school of their choice (e.g. charter schools)."

The diversity of opinions on that last one make it particularly worthwhile.

Where do I get a forecast of ENSO?

My colleagues and I have been pushing the idea that ENSO forecasts should be broadly integrated into economic, security and social policies in the tropics and subtropics.  In a talk to policy folks yesterday, I tried to point them towards an excellent resource provided by the International Research Institute for Climate and Society (at Columbia) that aggregates ENSO forecasts across many modeling groups (here).  Their current forecast for 2012 (Sep-Nov) is a 27% chance of El Nino, a 52% chance of neutral conditions and a 21% chance of La Nina.

More resources here.

Historical projections from many different models (both physics-based dynamical models and statistical models) with actual observations overlaid in black:

Forecasts from various models going out until October of next year:

For a reference on our ability to forecast ENSO, see Chen et al. They reconstruct forecasts using the LDEO model going back more than a century:

Time series of SST anomalies averaged in the NINO3.4 region (5° S–5° N, 120–170° W). The red curve is monthly analysis of [reconstructed observations] and the blue curve is the LDEO5 prediction at 6-month lead. Source: Nature.

and show, somewhat incredibly, that strong El Nino events can be reasonably forecast (corr ~ 0.75) up to twenty months in advance:

These are shown as a function of start month and lead. The straight green lines denote the verification month of May. The left panel is based on all monthly anomalies, while the right panel is for anomalies with amplitudes greater than 0.7 °C. The colour bar shows the range of correlation coefficients. Source: Nature.

Are we producing negative wealth?

Environmental Accounting for Pollution in the United States Economy
Nicholas Z. Muller, Robert Mendelsohn and William Nordhaus

Abstract: This study presents a framework to include environmental externalities into a system of national accounts. The paper estimates the air pollution damages for each industry in the United States. An integrated-assessment model quantifies the marginal damages of air pollution emissions for the US which are multiplied times the quantity of emissions by industry to compute gross damages. Solid waste combustion, sewage treatment, stone quarrying, marinas, and oil and coal-fired power plants have air pollution damages larger than their value added. The largest industrial contributor to external costs is coal-fired electric generation, whose damages range from 0.8 to 5.6 times value added.

Child labor and empirical development

Sol and I each recently come across empirical papers on child labor which, at least at first glance, seem to arrive at rather different conclusions. The first, by Eric Edmonds and Norbert Schady, finds that cash transfers to poor women in Ecuador decrease child labor rates (h/t Chris Blattman):

Poor women with children in Ecuador were selected at random for a cash transfer equivalent to 7 percent of monthly expenditures. The transfer is greater than the increase in schooling costs at the end of primary school, but it is less than 20 percent of median child labor earnings in the labor market. Poor families with children in school at the time of the award use the extra income to postpone the child’s entry into the labor force. Students in families induced to take-up the cash transfer by the experiment reduce their involvement in paid employment by 78 percent and unpaid economic activity inside their home by 32 percent.

The second is by Leah Nelson and shows that medium term credit for poor Thai families increases child labor rates:

This paper seeks to understand household business decisions in response to increased credit access in an environment with multiple market failures. A simple model suggests that households at certain wealth thresholds might be able to overcome the fi xed costs of entering entrepreneurship when they have increased access to credit. In the presence of labor market imperfections however, these same households may also be more likely to employ child labor. I test these predictions using household and child level panel data from Thailand. To isolate the causal impacts of household borrowing, I exploit the exogenous timing and institutional features of the Million Baht Program, one of the largest government initiatives to increase household access to credit in the world. I find that, consistent with the model, expanded access to credit raises entry into entrepreneurship for households in specific wealth groups while simultaneously increasing the use of child labor in these households. The results suggest that through the avenue of encouraging entrepreneurial activity, expanding credit access may have unintended consequences for the supply of child labor.

So at least according to these two studies there's a fairly substantial difference between simply providing money and providing credit that requires a productive return over a short horizon. That may seem obvious ex-post, but I think it'd be hard to predict without actually running the empirics and seeing how they shake out. Or, put differently: ever more and better empirical work provides ever better and more nuanced policy implications.

Consensus building and norm engineering

The New York Times has a fascinating article on a recent radical decline in the prevalence of female genital cutting / mutilation in Senegal:

Across the continent, an estimated 92 million girls and women have undergone [genital cutting]. But like more than 5,000 other Senegalese villages, Sare Harouna has joined a growing movement to end the practice.

The change has not yet reached Ms. Kande’s new home in her husband’s village, but if elders there pressured her to cut the baby girl she is taking into the marriage, she said, “I would resist them.” Her parents back her up.

“They would never dare do that to my granddaughter, and we would never allow it,” said Ms. Kande’s mother, Marietou Diamank.

The movement to end genital cutting is spreading in Senegal at a quickening pace through the very ties of family and ethnicity that used to entrench it. And a practice once seen as an immutable part of a girl’s life in many ethnic groups and African nations is ebbing, though rarely at the pace or with the organized drive found in Senegal. ...

[H]ere in Senegal, Tostan, a group whose name means “breakthrough” in Wolof, Senegal’s dominant language, has had a major impact with an education program that seeks to build consensus, African-style, on the dangers of the practice, while being careful not to denounce it as barbaric as Western activists have been prone to do.

The movement's success is heavily attributed to its inclusivity and consensus building, and the anecdotal evidence in the article seems to back it up. The fact that Tostan's strategy heavily involves griots, traditional story tellers who are somewhere between musician, MC, and radio personalities, seems particularly intuitive and appealing, a bit like trying to get celebrities and athletes in the US to speak out against smoking.

Along those lines, attributing of the program's success to having tailored itself to "African-style" consensus building seems rather small-minded. There are obviously many ways to get people to change their behavior (taxation, accolades, providing information ...), but abolishing genital mutilation seems to be a classic case of changing social norms, and when you think about it, a social norm is nothing more than a codification of expectations over the everyone's behavior. Changing a social norm involves shifting from one equilibrium state of expectations (everyone knows genital cutting is common and necessary and if my daughter doesn't undergo it she and my family will be judged) to another (everyone knows genital cutting is dangerous and unnecessary and if someone asks my daughter to do it they're crazy) for the entire group of individuals. It is, by definition, establishing a consensus. 

I think we can thus say that Tostan's success (to the extent that the success is Tostan's and not attributable to larger demographic forces) lies less in exploiting some native preference for consensus that "Africans" have than in focusing efforts on small clusters of individuals with correlated norms (villages), and then propagating that norm change through the next-closest set of clusters (neighboring / inter-marrying villages). That Tostan works to convince those individuals in an inoffensive way (try convincing anyone, anywhere, to stop doing something because it's 'barbaric') seems necessary but not sufficient to effect that change. 

Reconsidering the war on drugs

Last week, the Global Commission on Drug Policy published their Report which argues that a "War on Drugs" policy is an inefficient use of public resources.  The commission is an impressive group (which includes, among others, Kofi Annan, Paul Volcker and Richard Branson) but the report has been [predictably] rejected by leaders in the US and Mexico (despite the comission being convened by Latin American leaders that include former Mexican President Ernesto Zedillo).  I was intrigued by the fact that these leaders were explicitly trying to maximize social welfare subject to budget constraints, so I took a look for myself.  I particularly appreciate the report's first "principle":

Drug policies must be based on solid empirical and scientific evidence. The primary measure of success should be the reduction of harm to the health, security and welfare of individuals and society. 

In the 50 years since the United Nations initiated a truly global drug prohibition system, we have learned much about the nature and patterns of drug production, distribution, use and dependence, and the effectiveness of our attempts to reduce these problems. It might have been understandable that the architects of the system would place faith in the concept of eradicating drug production and use (in the light of the limited evidence available at the time). There is no excuse, however, for ignoring the evidence and experience accumulated since then. Drug policies and strategies at all levels too often continue to be driven by ideological perspectives, or political convenience, and pay too little attention
to the complexities of the drug market, drug use and drug addiction....  

click to enlarge

This reminds us that drug policies were initially developed and implemented in the hope of achieving outcomes in terms of a reduction in harms to individuals and society – less crime, better health, and more economic and social development. However, we have primarily been measuring our success in the war on drugs by entirely different measures – those that report on processes, such as the number of arrests, the amounts seized, or the harshness of punishments. These indicators may tell us how tough we are being, but they do not tell us how successful we are in improving the ‘health and welfare of mankind’.

I found the report thoughtful and the overall conclusions eerily reminiscent of John D. Rockefeller Jr.'s 1932 remark during Prohibition

When Prohibition was introduced, I hoped that it would be widely supported by public opinion and the day would soon come when the evil effects of alcohol would be recognized. I have slowly and reluctantly come to believe that this has not been the result. Instead, drinking has generally increased; the speakeasy has replaced the saloon; a vast army of lawbreakers has appeared; many of our best citizens have openly ignored Prohibition; respect for the law has been greatly lessened; and crime has increased to a level never seen before.

and reflected in this simple wikipedia graph

I don't think any of this is surprising to economists.  The demand for drugs is highly inelastic, so prices will rise dramatically if supply is restricted.  The burden of "taxation" (in this case, the energy the drug industry must expend to evade tough policies) is easily passed from suppliers onto consumers in the form of higher prices.  No surprises here, just basic principles from public finance.

Below are the key findings of the report.

Our principles and recommendations can be summarized as follows: 
End the criminalization, marginalization and stigmatization of people who use drugs but who do no harm to others. Challenge rather than reinforce common misconceptions about drug markets, drug use and drug dependence.

Encourage experimentation by governments with models of legal regulation of drugs to undermine the power of organized crime and safeguard the health and security of their citizens. This recommendation applies especially to cannabis, but we also encourage other experiments in decriminalization and legal regulation that can accomplish these objectives and provide models for others.

Offer health and treatment services to those in need. Ensure that a variety of treatment modalities are available, including not just methadone and buprenorphine treatment but also the heroin-assisted treatment programs that have proven successful in many European countries and Canada. Implement syringe access and other harm reduction measures that have proven effective in reducing transmission of HIV and other blood-borne infections as well as fatal overdoses. Respect the human rights of people who use drugs. Abolish abusive practices carried out in the name of treatment – such as forced detention, forced labor, and physical or psychological abuse – that contravene human rights standards and norms or that remove the right to self-determination.  

Apply much the same principles and policies stated above to people involved in the lower ends of illegal drug markets, such as farmers, couriers and petty sellers. Many are themselves victims of violence and intimidation or are drug dependent. Arresting and incarcerating tens of millions of these people in recent decades has filled prisons and destroyed lives and families without reducing the availability of illicit drugs or the power of criminal organizations. There appears to be almost no limit to the number of people willing to engage in such activities to better their lives, provide for their families, or otherwise escape poverty. Drug control resources are better directed elsewhere.

Invest in activities that can both prevent young people from taking drugs in the first place and also prevent those who do use drugs from developing more serious problems. Eschew simplistic ‘just say no’ messages and ‘zero tolerance’ policies in favor of educational efforts grounded in credible information and prevention programs that focus on social skills and peer influences. The most successful prevention efforts may be those targeted at specific at-risk groups.

Focus repressive actions on violent criminal organizations, but do so in ways that undermine their power and reach while prioritizing the reduction of violence and intimidation. Law enforcement efforts should focus not on reducing drug markets per se but rather on reducing their harms to individuals, communities and national security. 

Begin the transformation of the global drug prohibition regime. Replace drug policies and strategies driven by ideology and political convenience with fiscally responsible policies and strategies grounded in science, health, security and human rights – and adopt appropriate criteria for their evaluation. Review the scheduling of drugs that has resulted in obvious anomalies like the flawed categorization of cannabis, coca leaf and MDMA. Ensure that the international conventions are interpreted and/or revised to accommodate robust experimentation with harm reduction, decriminalization and legal regulatory policies.

Break the taboo on debate and reform. The time for action is now. 

A new mechanism to consider when measuring climate impacts on economies

[A shorter (and more heavily copy-edited) version of this post was published in EARTH Magazine, read it here.]

My paper Temperatures and tropical cyclones strongly associated with economic production in the Caribbean and Central America was recently published in the Proceedings of the National Academy of Sciences. Because the paper is a little technical, here is a presentation of the results that everyone should be able to understand.

Following countries over time, years with higher than

normal temperatures during the hottest season 

(Sep-Oct-Nov) exhibit large reductions in output across  

several non-agricultural industries.

Central finding:
Economic output across a range of industries previous thought of as "not vulnerable to climate change" respond strongly to changes in temperature.  The data suggest that the response is driven by the direct human response to high temperatures: people generally are less productive and tire faster when it's hot.  This impact, which appears to be quite large, has not been factored into any previous estimates for the global cost of climate change.

Background
Governments and organizations around the world are trying to figure out how much money we should spend to avoid climate change.  The answer isn't obvious.  On the one hand, climate change seems ominous and we'd like to spend lots of money to avoid it. But on the other hand, if we spend money on avoiding climate change, we can't spend it on other important things. For example, imagine that the United Nations has a million dollars it can spend. Should it spend it on building solar panels or building schools?  Both are clearly important. But if we want to get the most "bang for our buck," we need to figure out what the benefits of these two types of investments are.

A whole research industry has sprung up around the cost-benefit analysis of preventing climate change.  How much money should be spent to prevent climate change by investing in more expensive low-carbon technologies? Who should pay for it and when should they pay for it?  A tremendous amount of intellectual machinery has been applied to this problem by many extremely smart people.  The basic approach is to build models of the world economy-climate system and try to see what happens to the climate and the economy under different global policies.  These models are used by governments around the world to determine what they think the best climate policies are and how much they should spend on the problem.

However, there is something of a dark secret to this approach: we don't really know what will happen to us if the climate changes.  We have a fairly good grasp of how much it might cost to implement different energy policies. And we've learned a lot about how different energy policies will translate into global climate changes.  But when it comes to figuring out how those climate changes translate into costs to society (both financial and non-monetary), we end up having to do a lot of guesswork.

It's unfair to say we know nothing about the costs of climate change, but what we understand well is limited to certain types of impacts.  For example, we have been doing extensive research on the possible agricultural impacts for years. We've also done studies for a lot of the health impacts.  But most research stops there.  For example, we only are beginning to learn about the effect of climate on people's recreation and perceived happiness.  We're also only beginning to learn about the effect of climate on violence and crime.  We know a lot (but not nearly everything) about the effect of climate on ecosystems, but we don't really understand how ecosystems affect us, so we still can't estimate this impact on society. The list goes on.

Now we know a lot about climate impacts on health and agriculture because people have studied those impacts a lot.  Why did we study those kinds of impacts so much? I'm not sure. Maybe because the importance of climate on health and agriculture is obvious (eg. my plants on the windowsill died after just two days of this summer's heat wave).

The fact that we only really understand agricultural and health impacts of climate change is very important in the cost benefit analyses I mentioned earlier.  When governments are trying to figure out the best policies, they add up the known costs of preventing climate change and they add up the known benefits of preventing climate change.   If the costs outweigh the benefits, then that suggests we shouldn't spend much money to stop climate change.  But there is a natural asymmetry in this comparison between costs and benefits: we know all (or most of) the costs but only know the health and agricultural benefits.  So when we add up the costs of energy policies, the numbers tend to look very big.  But when we add up the known benefits of those policies, we add up the health benefit and the agricultural benefits, but we have to stop there because we don't know what else will be affected by climate change.  Maybe it shouldn't be surprising that many cost-benefit analyses find that climate change is not worth spending a lot of money on.

But what we know about climate impacts in non-health and non-agricultural sectors is slowly improving.  In a 2009 working paper, Dell, Jones and Olken did something very simple and got very surprising results.  They compared the economic output of countries over time with year-to-year changes in the weather of those countries.  They found that in poor countries, small increases in the annual average temperature of a country lead to large drops in economic output of that country.  The approach sounds simple, right? It is.  But the results are startling because they found such a large effect of temperature. They estimate that a 1C increase in average temperatures decreases a poor country's gross domestic product (GDP) by 1.1% in the same year. To get a sense of how big this effect is, recall that the economy of the Unites States shrank by 2.4% in 2009 and people are upset about the state of the economy.

Because the effect found by Dell et al. is so large, many people have been skeptical that it represents something real (note from my own unpublished work: I can corroborate their results using different data sets from the ones they used).  To check these results further, in 2010, Jones and Olken tried to looking for a similar effect in the exports of these countries and found that they also responded strongly to temperature changes.  Do people believe the general result yet? I'm not sure.  But part the skepticism seems to persist because its hard to know why poor countries should be so strongly affected by temperature.  One reason for this is that it's very hard to know what mechanisms are at work when one is only looking at macro-economic data.  Further, thinking of ways in which temperature affects economics this strongly and systematically across countries seems to be hitting the limits of many peoples' imaginations. This is where my study comes in.

Geoengineering?

I was just on the website of EARTH Magazine (a magazine published by the American Geological Institute and whose target audience is the scientifically-inclined) and in the upper right corner was a simple online poll for visitors to click on.  The question and results are below.  I'm not even sure how I feel about geoengineering, but I find this simple graph fascinating.  

Revisiting Project Star and the Long-Term Impacts of Educational Interventions

Some big news in educational economics came out this week with a team of economists including Raj Chetty and Emmanuel Saez releasing some pretty groundbreaking results on the long-term impacts of Tennessee's Project Star. Slides from the talk where the results were presented are here. The New York Times has a pretty good overview here. Project Star is pretty well known in economics as it was one of the first big purposely randomized education experiments. Students were randomly placed in classes with different numbers of students to try and tease out whether class size had an effect on educational outcomes ("Star" pithily stands for "Student Teach Achievement Ratio"). It famously found that (a) effects were there in the intuitive sense, i.e., students in smaller classes did better on standardized exams, the metric of choice, but (b) those effects trailed off over time.

What Chetty, Saez and their coauthors do (which I think captures pretty nicely the sort of economics that sends a tingle of cleverness-excitement up my spine) is match students who were in the Star experiment with their tax records to get long term earning data. Two things are particularly cool about this. The first are the results themselves. They find that not only are class size effects important in the long run but teacher quality effects and (even more robustly) class quality effects have very measurable long-term impacts on latter life earnings and college attainment / achievement rates, even when those effects take place in kindergarten. If you want the terrifyingly strong result, check out slides 44 and 49 in the pdf. Moreover, they go back and show that test scores, which everyone agrees are only a proxy for what we actually care about, do a fairly nice job of predicting how people will do later on, providing some validity to programs that measure their impact by how well students do on exams. Whether that should be exciting or troubling is, I think, a pretty deep question.

The second thing that's cool about it is that it sets out to find long-term, indirect, but ultimately very important effects in a very picked-over area of research (Star has been written about a *lot*). The education literature has long been one of the main sources of new applied econometric technique, which is why someone like me whose research has very little to do with education knows about things like Tennessee Star or class size cutoffs in Israel. So the precedent this sets, which is basically "sometimes the effects we care about are 20 years down the line and not in the original area we were looking at" comes in a heavily trafficked area of research with both rich existing precedent and a lot of sway over other areas of applied research. Which obviously makes me happy, since that same precedent can be thought of as applying to a lot of the environmental, health, and public goods questions that we concern ourselves with in sustainable development-related research.

Anyway, if you have a second read the Times article or, even better, flip through the slides (they're pretty clean and approachable and have some great graphs). Then go teach a five year old something new.