Writing in the NYT, Christina Romer (professor at UC-Berkeley and former chair of President Obama’s Council of Economic Advisers) finds the justification for a "manufacturing policy" to be wanting:
As an economic historian, I appreciate what manufacturing has contributed to the United States. It was the engine of growth that allowed us to win two world wars and provided millions of families with a ticket to the middle class. But public policy needs to go beyond sentiment and history. It should be based on hard evidence of market failures, and reliable data on the proposals’ impact on jobs and income inequality. So far, a persuasive case for a manufacturing policy remains to be made, while that for many other economic policies is well established. Where is she wrong?

Over at Grist, my long-time critic David Roberts does some simple energy math and finds that emissions reductions will be difficult because energy efficiency gains, while undoubtedly a good thing, won't make much of a dent in reducing emissions. Roberts might have saved himself some time by starting with The Climate Fix;-)

The numbers lead Roberts to conclude that we need to engage in a process of global economic contraction. Once he works through that math, he'll find his choices are (a) to keep poor people poor and make rich people poor, or (b) to focus on technological innovation to accelerate the decarbonization of economic activity. Where ever he comes out on that debate, (a) isn't going to happen -- iron law and all that.

But seriously, kudos to David for taking the time to run the numbers and report the results -- we all benefit from such analyses, uncomfortable as the results might be.

With the admission by public officials today that the Wivenhoe dam was indeed mismanaged,  The Australian newspaper is right to trumpet the importance of old school investigative journalism. Without their work, it is likely that the mismanagement would not have been uncovered:
Whatever its findings, the [Queensland flood investigation] report will be more informative and comprehensive as a result of the inquiry being reconvened and extended for 13 days after The Australian exposed glaring inconsistencies in the original evidence given by SEQWater and flood engineers about serious breaches of the dam's operating manual over two days leading up to the disaster. . .

For the public, an alarming aspect of the issue is that the mismanagement was uncovered not by their elected representatives or through the initial inquiry hearings, but by senior journalist Hedley Thomas's painstaking reading of official records. These suggested that SEQWater remained locked into the wrong strategy over the weekend of January 8 and 9 and into early Monday before the Brisbane River first broke its banks on January 11.

Scepticism, scrutiny of records and refusing to accept official spin are the hallmarks of fine journalism. Four days after the river peaked, contrary to SEQWater's insistence that the operating manual had been followed, Thomas questioned why the operation of the dam failed. He also reported independent engineer Michael O'Brien's view that catastrophe would have been avoided if releases had been adequate. Such probing, alas, did not suit more gullible media outlets, including Crikey, which brushed the public interest aside in claiming our coverage was "distorted" by "out-of-control" ego. A year on, the operations manager and chief executive of Queensland's WaterGrid admit that, based on what they were told at the time, the dam was mismanaged for two crucial days before the floods. In a land of climate extremes, hard lessons have been learned about managing the ravages of floods as well as drought, and the evacuation of St George shows authorities are being proactive. The emergence of the truth about Wivenhoe is also a lesson about public-interest journalism. Whatever one might think about the political views found on the pages of The Australian or feelings about its ownership, Australians have been well served by its dogged reporting in the case of Wivenhoe. For everyone, the case provides a good example why independent oversight of experts and government makes good sense.

Figure. Annual aggregate insured losses (AUD$ million) for weather-related events in the Disaster List for years beginning 1 July with losses normalised to season 2011/12 values.

Ryan Crompton of Risk Frontiers at Macquarie University has provided an update of their normalized loss catalog which is shown in the graph above. Crompton sends along this description of the update:
The normalised loss figure shown above is an updated version of that published in Crompton and McAneney (2008). The methodology used to normalise losses has been refined and the loss data from seasons 2006/07 - 2010/11 has been included and normalised to season 2011/12 values. The Insurance Council of Australia (ICA) insured loss data is current as at 31/1/12.

In our previous normalisation of the Disaster List ending at the 2005/06 season (Crompton and McAneney, 2008) we noted the low loss activity in the most recent 5 seasons analysed. Since that time there has been heightened weather-related loss activity with the most recent 5 seasons to 2010/11 averaging slightly more than double the 45-year average. The average annual weather-related insured loss over the most recent 10 seasons (2001/02 – 2010/11) is within approximately 30% of the average annual loss over the full 45-year period of the Disaster List.

UPDATE 5 Feb: The revelations from the inquiry continue.

I have commented occasionally on the role of flood management decisions leading up to the flooding of Brisbane in early 2011, as it is a fascinating case study at the intersection of science, uncertainty, decision processes, accountability and politics. As I mentioned last week the official investigation was re-opened after emails were released that suggested some inconsistencies in earlier reporting. The re-opened investigation started yesterday with explosive revelations:
In a series of heated exchanges at Queensland's recalled floods inquiry yesterday, SEQWater's principal engineer of dam safety, John Tibaldi, was grilled over a report he penned in the weeks following the January floods, which accounted for the actions he and his fellow engineers took.

At one point, Mr Tibaldi choked up in tears under the questioning.

Commissioner Cate Holmes, a Supreme Court judge, reconvened the inquiry after The Australian revealed evidence that appeared to show the dam was employing less severe flood mitigation strategies than those detailed in Mr Tibaldi's report.

As well as SEQWater officials and engineers being called to testify, Premier Anna Bligh has been asked to submit a written statement to the inquiry. Ms Bligh said she would provide a comprehensive statement by Monday and would submit a copy of her diary and relevant documents relating to the meetings and briefings she attended at the time of the floods.

Mr Tibaldi told the inquiry the report used raw data collected during the flood -- including lake levels and outflows -- and he then matched the data to the release strategies prescribed in the dam manual, known as W1, W2, W3 and W4. He said he had no recollection of asking the three other dam engineers which strategies they were using at various times during the disaster, but prepared the report based on the raw data and subsequently sought their approval.

"I tried to match the strategy transitions against the data that was available to me (and) just made conclusions based on that data as to when strategy transitions had occurred," he said.

Counsel assisting the inquiry, Peter Callaghan SC, suggested the manual was therefore used to analyse and justify the decisions taken by the four engineers -- Mr Tibaldi, Robert Ayre, Terry Malone and John Ruffini -- rather than dictating the decisions they took at the time. Apparently SEQ Water is privately insured, though for what contingencies and to what financial level it is not clear from what I have read. What does seem increasingly clear is that someone is going to receive a big bill to settle what will inevitably be large claims against SEQ Water. Stay tuned.

Economist: I think you are way too optimistic that investments in technological innovation funded by a low carbon tax can lead to accelerated decarbonization of the economy. That is why I favor a high carbon price.

Me: But isn't the point of the high carbon price to stimulate innovation?  The question is thus how to stimulate or motivate that innovation. I think a high carbon price is politically impossible, which is why I argue for starting low with investments in innovation as part of the package.

Economist: A high carbon price will create incentives to change people's behavior. If prices are set appropriately the market will take care of the rest.

Me: But if you do not think that technological innovation can lead to an accelerated deacrbonization of the economy, what difference would it make if that innovation is stimulated by pricing or direct investment?

Economist: Pricing has reduced pollution in many areas. We just need to get the carbon price right.

Me: But I am curious about the causality implicit in your argument -- let me ask, of the four levers in the Kaya Identity [Population, Per capita wealth, Energy intensity, Carbon intensity] which ones do you see will be influenced by carbon pricing in a way that reduces emissions?

Economist: Well ... I guess carbon intensity and energy intensity.

Me: So then you do think that technological innovation can lead to accelerated decarbonization since carbon intensity and energy intensity are modulated by innovation?

Economist: Well, no, not at all. I don't think that the solution can be technological. I do think that pricing makes a lot more sense than focusing on technology.

Me: Can you believe all the rain?

The Australia Institute has released a very nice report by Richard Denniss titled, The use and abuse of economic modelling in Australia: Users' guide to Tricks of the Trade (PDF).  The essay illustrates its critique with several recent cases related to claims about jobs in the mining industry, the poker machine industry and as a consequence of the carbon tax.

Here is an excerpt:
Economic modelling has, for many people involved in Australian policy debates, become synonymous with the process of serious policy development. Proponents of policy change that are armed with economic modeling are often taken more seriously than those with 20 years experience working on the same problem. The modelling result that suggests tens of thousands of jobs will be lost or created often trumps logic or experience that suggests such claims are nonsensical.

This is not to suggest that modelling has no role to play in policy debates. It can and it does often make a useful contribution, but the fact that it sometimes can should not be confused with the conclusion that it always will. Indeed, in recent times some of the claims based on 'economic modelling' that has been made in debates such as the likely impact of poker machine reform or the introduction of a carbon price can only be described as nonsense.

The problem has become, however, that in an era in which segments of the media no longer have the time or inclination to examine claims before they are reported bad economic modelling is preferred by many advocacy and industry groups to good economic modelling for three main reasons:

1. it is cheaper
2. it is quicker
3. it is far more likely to yield the result preferred by the client

That said, bad economic modelling is relatively easy to identify if readers are willing to ask themselves, and the modeller, a range of simple questions. Indeed, it is even easier to spot when the modeler can't, or won't, answer such simple questions. Economic models, like all models, can be very useful. But they can also be used in ways that are misleading or just plain wrong. Denniss provides some good advice for recognizing the difference.

People familiar with my work, such as in The Honest Broker and The Climate Fix, will also be familiar with my interest in unpacking issues and problems into comprehensible bits. To that end in the area of innovation I am going to be posting on some definitional issues to simply sort through a number of basic propositions as a matter of clarifying my own thinking and writing.

In this post I am exploring the definition of a "job" -- which is a concept that carries considerable political importance and is a variable that we'd like to modulate via policy, but which typically falls into the category of "too obvious to define precisely."

What is a job?  Let's start with the following definitions related to employment offered  by the US government's Bureau of Labor Statistics (emphasis in the original):
The basic concepts involved in identifying the employed and unemployed are quite simple:

• People with jobs are employed.
  
• People who are jobless, looking for jobs, and available for work are unemployed.
  
• People who are neither employed nor unemployed are not in the labor force.
  

The survey is designed so that each person age 16 and over who is neither in an institution (for example, correctional facilities and residential nursing and mental health care facilities) nor on active duty in the Armed Forces is counted and classified in only one group. The sum of the employed and the unemployed constitutes the civilian labor force. Persons not in the labor force combined with those in the civilian labor force constitute the civilian noninstitutional population 16 years and over. These definitions, which date to 1942 (source), are extremely useful because they clearly define how the government views employment, which is the variable that policy makers seek to modulate when talking about "jobs." But these definitions don't quite get us to a fundamental definition of "jobs."

Here is what the BLS says about jobs:
Not all of the wide range of job situations in the American economy fit neatly into a given category. For example, people are considered employed if they did any work at all for pay or profit during the survey week. This leads me to the following description of a job:

The defining characteristic of a job is an exchange between and employer and an employee, of wages (or some other compensation) for services (economists like to call such services "labor," defined variously in terms of skills, knowledge, capabilities, etc.). Governments regulate such services in many ways (e.g., some services may be disallowed -- think hit men, drug dealing or prostitution) and the terms of employment are also regulated (e.g., minimum wage, occupational safety, etc.).

All jobs are thus service jobs. With that as a starting point, we are in a position to ask ourselves, in what ways should we categorize and classify jobs in order to help realize the various objectives of public policy? The answer to this question is not obvious, and it is not clear to me that the official government categories are necessarily the most useful or helpful for thinking about policies related to jobs -- recent discussions here related to "manufacturing" are one example.

I am trying to figure out (a) what a "manufacturing job" is, and (b) why many economists think that such jobs are in some way a special category of jobs.

My emerging view is that all jobs are service jobs and some such jobs involve the manipulation of tangible goods. In our economic accounting we classify some (but far from all) of those jobs that involve the manipulation of tangible goods (for instance, those that can be put into a shipping container) as manufacturing jobs, and others (such as in construction) as services. The distinction seems somewhat (entirely?) arbitrary to me and as apt to mislead as clarify our discussions of innovation and the economy.

Here is a specific example to discuss (Thanks AC!):
In this sprawling facility on Route 128, sporty Kia coupes and Volvo trucks are regularly taken apart and reassembled. Caterpillar tractors and Harley-Davidson motorcycles are put through exacting trials that test the latest advances in power steering and antilock brakes. Both Aston Martin Racing and the Penske Racing Team come here to shave seconds off their times.

But the 1,000-plus employees at PTC never touch a wrench or ball-peen hammer. Instead they develop and advance software that allows automakers to design, build, and service the latest automobiles rolling off production lines all over the world.

“The actual making of cars has moved to other parts of the world,’’ said Sin Min Yap, PTC’s vice president for automotive market strategy, “but the digital making of cars is thriving here.’’ Are the jobs at PTC "manufacturing jobs"? Are they "service jobs"? And, most importantly, why does such a distinction matter for our discussion of innovation, the economy and employment? (For initial background, here is how the North American Industry Classification System characterizes manufacturing.)

My view -- all jobs are service jobs. I will follow up on the consequences of such a view in subsequent posts.

Last week the US Energy Information Agency published an "early release" of its 2012 Annual Energy Outlook, which includes the agency's projections for various energy statistics out to 2030, based on a range of assumptions. The report also includes projections of carbon dioxide emissions to 2035, which allows for a comparison of the Obama Administration's commitments to targets for emissions reduction for 2020, 2025 and 2030 (the formal commitment made under the UN FCCC is here).

The graph at the top of this post shows the U.S. government's emissions projections (black line) and the emissions reduction targets (red, blue and green). In case you were wondering how big the misses are with respect to the targets in some sort of intuitive way, I've provided a measure of the magnitude of the shortfall (using the same methods described in depth in The Climate Fix) in terms of the number of coal power plants that would have to be replaced with nuclear power plants to meet the targets. (If you'd like to replace gas power plants, the numbers are about 40% more, due to the lower carbon intensity of gas generation. If you'd like to use wind turbines or solar power, well, get out a big calculator;-).

It should be fairly obvious that under the assumptions of the EIA (such as positive economic growth) that the emissions reduction targets are not going to be met. Given President Obama's renewed commitment to an "all of the above" strategy for energy production in the United States, is it finally time to dismiss the charade of emissions reductions targets and adopt a different approach?

Courtesy of The Chronicle of Higher Education, the graph above shows a time series of the self-reported political orientation of college freshman in the United States. There is a subtle hint of an increased polarization at the end of the G. W. Bush presidency, somewhat reversed in the time since. Like the US as a whole, the perspectives are dominated by those who describe their political views as "middle of the road."

A reader (Thanks JZ!) sends in links to Mike Rowe's Ted talk (at the bottom of this post) and Congressional testimony from last year (above). He is a champion of skilled labor and a great spokesman for parts of the workforce that are often marginalized as less worthy than college graduates.

Here is an excerpt:
I believe we need a national PR Campaign for Skilled Labor. A big one. Something that addresses the widening skills gap head on, and reconnects the country with the most important part of our workforce.

Right now, American manufacturing is struggling to fill 200,000 vacant positions. There are 450,000 openings in trades, transportation and utilities. The skills gap is real, and it's getting wider. In Alabama, a third of all skilled tradesmen are over 55. They're retiring fast, and no one is there to replace them.

Alabama's not alone. A few months ago in Atlanta I ran into Tom Vilsack, our Secretary of Agriculture. Tom told me about a governor who was unable to move forward on the construction of a power plant. The reason was telling. It wasn't a lack of funds. It wasn't a lack of support. It was a lack of qualified welders.

In general, we're surprised that high unemployment can exist at the same time as a skilled labor shortage. We shouldn't be. We've pretty much guaranteed it.

In high schools, the vocational arts have all but vanished. We've elevated the importance of "higher education" to such a lofty perch that all other forms of knowledge are now labeled "alternative." Millions of parents and kids see apprenticeships and on-the-job-training opportunities as "vocational consolation prizes," best suited for those not cut out for a four-year degree. And still, we talk about millions of "shovel ready" jobs for a society that doesn't encourage people to pick up a shovel.Here is an entertaining Ted talk by  Rowe (skip to 15:30 for the bottom line).

The Think Tanks and Civil Societies Program at the University of Pennsylvania has released its annual global rankings of think tanks around the world (here in PDF). Here are some top line findings.

Which countries have the most think tanks (global total = 6,545)?
1 United States 1815
2 China 425
3 India 292
4 United Kingdom 286
5 Germany 194
6 France 176
7 Argentina 137
8 Russia 112
9 Japan 103
10 Canada 97 What are the top ranked think tanks worldwide?
1. Brookings Institution – United States
2. Chatham House (CH), Royal Institute of International Affairs – United Kingdom
3. Carnegie Endowment for International Peace – United States
4. Council on Foreign Relations (CFR) – United States
5. Center for Strategic and International Studies (CSIS) – United States
6. RAND Corporation – United States
7. Amnesty International – United Kingdom
8. Transparency International – Germany
9. International Crisis Group (ICG) – Belgium
10. Peterson Institute for International Economics – United States
11. German Institute for International and Security Affairs (Stiftung Wissenschaft und
Politik SWP) – Germany
12. International Institute for Strategic Studies (IISS) – United Kingdom
13. Heritage Foundation – United States
14. Cato Institute – United States
15. Woodrow Wilson International Center for Scholars – United States There is plenty more information on the global think tank "ecosystem" in the full report, here in PDF.

    
                   Tuesday, 7 February 2012
Time:       12:30 pm for 12:45 pm – 1:45 pm Place:      The Lowy Institute for International Policy                    Ground floor, 31 Bligh Street, Sydney RSVP:      Before 5.00pm on Monday, 6 February 2012
Please click here to accept or decline this invitation

Scientists in Policy and Politics
Scientists, and experts more generally have choices about the roles that they play in today's political debates on topics such as global warming, genetically modified foods, and food and drug safety, just to name a few. Professor in Environmental Studies at the University of Colorado, Roger Pielke will discuss how we can understand these choices, their theoretical and empirical bases, what considerations are important to think about when deciding, and the consequences for the individual scientist and the broader scientific enterprise.

Roger A. Pielke, Jr. has been on the faculty of the University of Colorado since 2001 and is a Professor in the Environmental Studies Program and a Fellow of the Cooperative Institute for Research in Environmental Sciences (CIRES) where he served as the Director of the Center for Science and Technology Policy Research from 2001-2007. Roger’s research focuses on the intersection of science and technology and decision making. In 2006, Roger received the Eduard Brückner Prize in Munich, Germany for outstanding achievement in interdisciplinary climate research. From 1993-2001 Roger was a Scientist at the National Center for Atmospheric Research. He holds appointments as a Research Fellow, Risk Frontiers, Macquarie University; Visiting Senior Fellow, Mackinder Programme, London School of Economics; and Senior Visiting Fellow at the Consortium for Science, Policy and Outcomes of Arizona State University. He is a Senior Fellow of the Breakthrough Institute. He is also author, co-author or co-editor of seven books, including The Honest Broker: Making Sense of Science in Policy and Politics published by Cambridge University Press in 2007. His most recent book is The Climate Fix: What Scientists and Politicians Won't Tell you About Global Warming.

Please join us for a lively and thought provoking discussion.

Text of both speeches from the New York Times and world cloud courtesy of WordItOut (top 100 words, minimum 5 characters).

State of the Union:
Republican response (Governor Mitch Daniels):

Writing in the current issue of The Atlantic, Adam Davidson has an absolutely brilliant article on the state of American manufacturing. It is a lengthy article that you should read in full. The article clearly explains why it is that manufacturing jobs are going away, even as the manufacturing sector strengthens. It also explores the challenges facing so-called unskilled workers in a big, rich 21st century economy. The article does this by looking at real people in a real factory.

Here is an excerpt from the piece:
Is there a crisis in manufacturing in America? Looking just at the dollar value of manufacturing output, the answer seems to be an emphatic no. Domestic manufacturers make and sell more goods than ever before. Their success has been grounded in incredible increases in productivity, which is a positive way of saying that factories produce more with fewer workers.

Productivity, in and of itself, is a remarkably good thing. Only through productivity growth can the average quality of human life improve. Because of higher agricultural productivity, we don’t all have to work in the fields to make enough food to eat. Because of higher industrial productivity, few of us need to work in factories to make the products we use. In theory, productivity growth should help nearly everyone in a society. When one person can grow as much food or make as many car parts as 100 used to, prices should fall, which gives everyone in that society more purchasing power; we all become a little richer. In the economic models, the benefits of productivity growth should not go just to the rich owners of capital. As workers become more productive, they should be able to demand higher salaries.

Throughout much of the 20th century, simultaneous technological improvements in both agriculture and industry happened to create conditions that were favorable for people with less skill. The development of mass production allowed low-skilled farmers to move to the city, get a job in a factory, and produce remarkably high output. Typically, these workers made more money than they ever had on the farm, and eventually, some of their children were able to get enough education to find less-dreary work. In that period of dramatic change, it was the highly skilled craftsperson who was more likely to suffer a permanent loss of wealth. Economists speak of the middle part of the 20th century as the “Great Compression,” the time when the income of the unskilled came closest to the income of the skilled.

The double shock we’re experiencing now—globalization and computer-aided industrial productivity—happens to have the opposite impact: income inequality is growing, as the rewards for being skilled grow and the opportunities for unskilled Americans diminish. Looking for significant job growth in a sector that is in the midst of experiencing a revolution in productivity gains is just bad math.

Edward Alden, writing at the new CFR blog, Renewing America, points to business services where future job growth has significant prospects:
[E]ven as the manufacturing sector will continue to grow, the United States will need to look to other industries for robust, higher-wage job growth. My bet is on the business services sector, in fields such as engineering services, movie and software production, and telecommunications where demand for U.S. services is growing rapidly, especially in the emerging markets. Brad Jensen of Georgetown University and the Peterson Institute has laid out the case in his excellent new book. These sectors already employ twice as many people at higher average wages than in manufacturing, and job growth has been strong over the past decade. The United States runs a steadily rising trade surplus in services, compared with a deep, chronic deficit in manufacturing trade. These are sectors in which the United States, along with Europe, has a strong comparative advantage and the potential to sell much more to the world.

No one sector is going to dig the United States out of the jobs hole we currently find ourselves. But manufacturing is a particularly poor candidate. With an estimated 40 million unskilled workers (according to Davidson in The Atlantic) the US has a big challenge ahead.

Writing at her blog, The Short Run, my superstar grad student Jessica Weinkle looks at recent catastrophe model filings in the state of Florida, as part of her dissertation research:
In America's deep south, a region not so far away, hides a new foe threatening otherwise intelligent people's ability to decide. The Louisiana Insurance Commissioner, Jim Donelone, has rung the alarm putting homeowners on alert of  "The looming threat of the new cat model, RMS 11".  This is the newest addition in the catastrophe model rogue gallery challenging the gallant efforts of state insurance regulating offices. The kryptonite in their coding is the incredible capacity to produce scientifically supported uncertainty thereby weakening the ability to control rates by politically hopeful insurance commissioners everywhere. A past episode between dueling regulating powers and risk predicting machinery demonstrated the societal cost inflicted by these dastardly foes creating uncertainty whenever plugged into a wall. In 2006, RMS rolled out an arbitrary change to their trusty hurricane catastrophe model in RiskLink 6.0, costing Florida homeowners $82 billion. Stay tuned to state regulating offices for the latest updates on the battle between man and machine...

In the mean time, let's take a closer look at these new trade secret rascals... Weinkle uncovers some eyebrow raising factiods, such as the fact that the estimated probability of a Category 5 hurricane hitting Florida has apparently increased from previous model filings in several models by 100%. She also shows that across five different models, the estimated cost of a Category 5 storm in Florida ranges from $18 billion to $146 billion.

Based on these numbers, Weinkle calls the catastrophe models tools that create uncertainties and makes the nonobvious point that decisions about risk are actually decisions about modeled risk -- which may or may not be the same thing:
Together, these models create a great deal of uncertainty about the risk being insured against.  In the world of insurance, uncertainty about the risk is risk in and of itself.  If uncertainty increases, then the cost will too and vice versa.  So, a reasonable question to ask would be, "Has the modeled risk changed?" Not surprisingly, catastrophe models have faced some criticism, such as found in this recent news article from Louisiana:
Catastrophe models are controversial. Proponents say they bring science to underwriting and synthesize the latest understanding of storms and climate change to insurers. Opponents say they're gee-whiz black boxes that manufacture instant justification for high rates for insurers. The problem with catastrophe models is not that they lack value (they are actually extremely powerful and potentially useful tools), it is just very hard to assess what that value is (e.g., PDF), and their black box nature makes such assessments extremely difficult. The lack of an industry-wide evaluation capability, strong hints of conflict of interest and the defensive nature of some of the cat modelers makes the issue a mine field of bad decisions for businesses and governments alike.

Just over a year ago, Brisbane, Australia experienced its worst flooding since 1974, resulting in billions of dollars in damage. Immediately after the event the focus of attention turned to the management of the Wivenhoe Dam, which was built after the 1974 floods to prevent a repeat.

This past week, as the Queensland flood commission is wrapping up it investigation, The Australian has uncovered evidence that the Wivenhoe managers operated the dam according to an incorrect procedure. Here is a summary from the Sydney Morning Herald:
On Monday and Tuesday The Australian newspaper alleged engineers operating the Wivenhoe Dam used the wrong water-release strategy, breaching its operation manual, in the lead-up to the January 2011 Brisbane flood.

It reported SEQWater engineers, who operate the dam, failed to move to a higher water release strategy early enough, contributing to the Brisbane and Ipswich floods.

The paper used emails between SEQWater and the WaterGrid to back up their claims.

It went on to accuse the commission of overlooking the documents and accepting at face value evidence from engineers who said the manual had been followed correctly.

The commission was in possession of the emails but did not make them publicly available. The release of the information contained in the emails has prompted a re-opening of the Floods Commission inquiry and a delay in the Queensland state election. Anna Bligh, the Premier of Queensland (pictured at the top of this post with Prime Minister Julia Gillard), is facing an electoral defeat based on polling, prompting calls that the election is being delayed for politically strategic reasons.

Writing very recently in the open-access journal Water, Robin van den Honert and John McAneney, of Macquerie University provide a comprehensive review and assessment of the 2011 floods and their impacts, and which is likely to serve as the definitive study of the event for some time to come. Here is the paper's abstract:
The 2011 Brisbane Floods: Causes, Impacts, Implications

On 13th January 2011 major flooding occurred throughout most of the Brisbane River catchment, most severely in Toowoomba and the Lockyer Creek catchment (where 23 people drowned), the Bremer River catchment and in Brisbane, the state capital of Queensland. Some 56,200 claims have been received by insurers with payouts totalling $2.55 billion. This paper backgrounds weather and climatic factors implicated in the flooding and the historical flood experience of Brisbane. We examine the time history of water releases from the Wivenhoe dam, which have been accused of aggravating damage downstream. The dam was built in response to even worse flooding in 1974 and now serves as Brisbane’s main water supply. In our analysis, the dam operators made sub-optimal decisions by neglecting forecasts of further rainfall and assuming a ‘no rainfall’ scenario. Questions have also been raised about the availability of insurance cover for riverine flood, and the Queensland government’s decision not to insure its infrastructure. These and other questions have led to Federal and State government inquiries. We argue that insurance is a form of risk transfer for the residual risk following risk management efforts and cannot in itself be a solution for poor land-use planning. With this in mind, we discuss the need for risk-related insurance premiums to encourage flood risk mitigating behaviours by all actors, and for transparency in the availability of flood maps. Examples of good flood risk management to arise from this flood are described. Based on the new reporting from The Australian on the possible errors in flood management and the comprehensive analysis in van den Honert and McAneney (2011), it is clear that bad decision making played a major role in the disaster. The bad decisions were the result of mismanagement, a deeply flawed management architecture, or what seems to be increasingly likely -- both.

If you read the New York Times, you might be led to believe that some experts think that Apple represents a lot that is wrong with the modern innovation economy. Here is an excerpt from yesterday's lengthy article on Apple:
Not long ago, Apple boasted that its products were made in America. Today, few are. Almost all of the 70 million iPhones, 30 million iPads and 59 million other products Apple sold last year were manufactured overseas. . .

Apple employs 43,000 people in the United States and 20,000 overseas, a small fraction of the over 400,000 American workers at General Motors in the 1950s, or the hundreds of thousands at General Electric in the 1980s. Many more people work for Apple’s contractors: an additional 700,000 people engineer, build and assemble iPads, iPhones and Apple’s other products. But almost none of them work in the United States. Instead, they work for foreign companies in Asia, Europe and elsewhere, at factories that almost all electronics designers rely upon to build their wares.

“Apple’s an example of why it’s so hard to create middle-class jobs in the U.S. now,” said Jared Bernstein, who until last year was an economic adviser to the White House. Let me just say – No it's not.

What kind of jobs does Apple and its suppliers have overseas? The NYT investigated and described a facility in China:
The facility has 230,000 employees, many working six days a week, often spending up to 12 hours a day at the plant. Over a quarter of Foxconn’s work force lives in company barracks and many workers earn less than $17 a day. When one Apple executive arrived during a shift change, his car was stuck in a river of employees streaming past. “The scale is unimaginable,” he said.

Foxconn employs nearly 300 guards to direct foot traffic so workers are not crushed in doorway bottlenecks. The facility’s central kitchen cooks an average of three tons of pork and 13 tons of rice a day. While factories are spotless, the air inside nearby teahouses is hazy with the smoke and stench of cigarettes.

Foxconn Technology has dozens of facilities in Asia and Eastern Europe, and in Mexico and Brazil, and it assembles an estimated 40 percent of the world’s consumer electronics for customers like Amazon, Dell, Hewlett-Packard, Motorola, Nintendo, Nokia, Samsung and Sony.

“They could hire 3,000 people overnight,” said Jennifer Rigoni, who was Apple’s worldwide supply demand manager until 2010, but declined to discuss specifics of her work. “What U.S. plant can find 3,000 people overnight and convince them to live in dorms?” What indeed? These are not “middle class jobs.”

Kraemer at al. (2011, here in PDF), by researchers at California-Irvine, Berkeley and Syracuse who have studied Apple’s supply chains, first for the iPod and then the iPhone and iPad, conclude:
Those who decry the decline of U.S. manufacturing too often point at the offshoring of assembly for electronics goods like the iPhone. Our analysis here and elsewhere makes clear that there is simply little value in electronics assembly. The gradual concentration of electronics manufacturing in Asia over the past 30 years cannot be reversed in the short- to medium-term without undermining the relatively free flow of goods, capital, and people that provides the basis for the global economy. And even if high-volume assembly expands in North America, this will likely take place in Mexico where there is already a relatively low-cost electronics assembly infrastructure. What has Apple done?

It has captured a significant fraction of the global market for mobile phones, as shown by the figure below from The Economist.
More importantly, Apple has created jobs in the United States. In 2006, before the iPhone was even on the market Apple as a company had less than 18,000 employees. In 2010, according to the NYT, Apple had 63,000 employees, with 43,000 in the United States. Even if we assume that all of its 2006 employees were in the US (they weren’t) Apple has created more than 25,000 jobs in the US. To put this data into perspective, at a time when the number employed in the US dropped by more than 5% Apple increased its US-based employment by more than 150%.

Rather than calling out Apple as an example of what is wrong in the innovation economy, we should be pointing to Apple as an example to emulate. The question that we should be asking is not how can we get Apple to hire more Americans, but rather, how do we get America to create more Apples?

Kerry Emanuel of MIT (WCAS in PDF) and Robert Medelsohn of Yale University, Emanuel and colleagues (Nature Climate Change), have new papers out on future hurricane damage. The findings of both papers reinforce existing literature on the very long time necessary to detect a signal of human caused climate change in the disaster record under recent projections and the relative role of the importance of development over human-caused climate change in future losses from tropical cyclones.

Emanuel (2011, PDF)  implemented an alternative methodology to Crompton et al. (2011) to assess under various scenarios when the signal of human-caused climate change would be detectable in the damage record of Atlantic hurricanes. He looked at four different models, and three of them showed increasing losses and one a small decrease. Of the three models that showed increasing losses the time until detection is 40, 113 and 170 years.

This time to detection is shorter than that which we found in Crompton et al. (2011). Why is that? Emanuel use an older set of model runs (we used Bender et al. 2010 -- I wonder why they used something different) and that probably accounts for the difference. It would have been nice if Emanuel had used the Bender runs, as that would have allowed an apples to apples comparison. I'd speculate that our numbers would be quite similar apples-to-apples.

Regardless, the two papers are in agreement that the time to detection of a signal of human-caused climate climate change, assuming that recent projections are correct, is a long, long time. Like, not in our lifetimes and certainly not now.

Mendelson et al. (2012) examine a range of scenarios for how tropical cyclone damage will increase to 2100. That paper concludes that tropical cyclone damage will decrease as a proportion of global GDP from 0.04% today to 0.01% in 2100 assuming human-caused climate change, using the same four models as used in Emanuel (2011, PDF). That is right, decrease as a portion of GDP. (Apparently, this result was not sexy enough for Nature Climate Change which headlined their homepage announcement of the paper rather misleadingly as, "Tropical Cyclone Damage Set to Double" referring to the expected increase in aggregate damages to 2100.) The paper also explores how damages might increase in regions around the world, though it is important to recognize that both the climate and socio-economic assumptions of the paper are highly speculative.

Mendelsohn et al. (2012) explain that their findings are consistent with existing work and as such, adds to our growing understanding that under a very wide range of scenarios for how climate might change and how society might develop, socio-economic factors will dominate the future damage record (see this paper in PDF for an unrealistically broad range of explored scenarios) independent of a wide range of assumptions and uncertainties.

Anyone claiming that they can see a human-caused climate signal in the hurricane damage record (or even the hurricane record itself) is facing a growing mountain of peer-reviewed research to overcome.

H/T to Revkin and Kloor