John Cochrane: The best of times or the worst of times?

John Cochrane's blog entry.

Joseph Stiglitz, like Paul Krugman, is a Nobel prize winning economist who has let his emotions overcome his intellect.  Cochrane illustrates this in his article.

You cannot conclude credibility from the fact that someone has a Ph.D or even that they have won a Nobel prize.
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So how is the economy doing? A good friend passed along for comment a recent project syndicate essay by Nobel Prize winning economist Joe Stiglitz. For an alternative view, I found interesting commentary on the CEA website, "The Impact of the Trump Labor market on historically disadvantaged Americans" and "The blue-collar boom reduces inequality"

A fact that cannot be missed is that overall GDP is growing. In terms of the overall economy, 2019 was the best year in all of human history. The 3.5% unemployment rate has not been this low since December 1969. So, if we wish to complain, it must be that this prosperity is not evenly shared. (I would also complain that things could be much better, but neither of our essays today is really about that point. Free-market paradise will have to wait.)

Stiglitz:

As the world’s business elites trek to Davos for their annual gathering, people should be asking a simple question: Have they overcome their infatuation with US President Donald Trump?

Two years ago, a few rare corporate leaders were concerned about climate change, or upset at Trump’s misogyny and bigotry. Most, however, were celebrating the president’s tax cuts for billionaires and corporations and looking forward to his efforts to deregulate the economy. That would allow businesses to pollute the air more, get more Americans hooked on opioids, entice more children to eat their diabetes-inducing foods, and engage in the sort of financial shenanigans that brought on the 2008 crisis.

Hmm. Before we get on to economics, let us parse that as rhetoric. In making any argument, one may try to convince the other side, one may to convince the great undecided middle, or one may try to rile up those already persuaded. If one wishes to convince the other side, or the middle, it is useful to start by reaching out -- I understand your worldview, we have common goals, we're all in this together, but here are some unsettling facts you should become aware of. One should work hard not to needlessly piss off a reader who starts on the other side of an argument. If you wish to convince me of Y, do not start by stating as fact an opinion about Z which I heartily disagree with, which is irrelevant to Y. Gain trust, do not squander it. Do not needlessly personalize, politicize, or demonize the argument. Don't assert evil motives, at least without evidence, and especially of those you wish to convince.

Well, it's pretty clear which strategy Stiglitz is following here! Before we get to any economics, it starts with Trump, and "infatuation." That corporate leaders are "infatuated" with Trump, and that few corporate leaders were concerned about climate change 2 years ago is a simple falsehood. Corporate leaders are cloyingly woke. Jamie Diamond of JP Morgan Chase, leading the business roundtable, announced in favor of "stakeholders" not shareholders. Goldman Sachs itself just announced it won't fund oil and gas in the Arctic, or US companies that don't have female or "diverse" boards. Oil companies tout their commitment to climate. "Trump's mysogyny and bigotry?" Well, if you are at all a Trump fan, by insulting your guy, it's clear at the outset this article is not for you. Stiglitz seems in full Trump derangement syndrome, and leads a reader to suspect what follows may not be totally balanced.

"Most, however, were celebrating the president’s tax cuts for billionaires and corporations." Z again. asserted as fact, and motivation. What if I think that cutting marginal rates was a good thing -- after all the Obama administration wanted to cut corporate taxes for years? What about the plain fact that those advancing tax cuts did not say "we want to give tax cuts for billionaires and corporations." They explained traditional economics that they wanted a greater incentive to invest, which would raise the capital stock which in time would raise wages. They were celebrating, in their words, Trump's growth-enhancing marginal rate reductions. You may think otherwise of the effect, but adding calumny and vilification to statements asserted as facts that half your readers disagree with is a sure way to cut out all but your base.

The reader must also, before proceeding to the argument, accept as common ground fact that anyone who thinks a bit of regulatory reform may be useful for the economy (me) has as their motivation a desire to "pollute the air more, get more Americans hooked on opioids, entice more children to eat their diabetes-inducing foods." I haven't been to Davos, but I wonder just how many cocktail parties there are where people debate, say "how do we get some more pollution up, and fatten the little children more?" Is that one next to the satanic rituals? Does Davos have no cocktail parties bemoaning climate change?

(I went on a bit here, as I have listened to quite a bit of the Democratic House manager's presentations to the Senate in the last few days. Are they trying to convince Republicans to vote their way, or are they playing to rile up a base in the next election? You choose.)

Well, let's read on, though we are clearly not invited here. What is the case? GDP is up, employment is up, wages are up especially among people of limited means. Where is the economic tragedy?

"US life expectancy, already relatively low, fell in each of the first two years of Trump’s presidency, and in 2017, midlife mortality reached its highest rate since World War II. This is not a surprise, because no president has worked harder to make sure that more Americans lack health insurance."

Stiglitz doesn't give a source. I found one at the world bank, and echoed at Fred

It only goes to 2017, so if the two years means 2017 and 2018 I don't know where the latter number comes from. But yes, life expectancy did dip.

2014: 78.84
2015: 78.69 (-0.15)
2016: 78.54 (-0.15)
2017: 78.54 (0.00)

Hmm. Who was in office in 2015 and 2016?

Anyway, this is a grand calumny. Yes, it is troubling to see life expectancy decline, and troubling that ours is less than some other countries. But the reasons are clear and well known. The US starts with much worse demographics. Scandinavia has longer life expectancy, but so does Minnesota. The main issues in these statistics are gunshot wounds, mostly from gang warfare and suicide, traffic accidents, and drug overdoses. Not health insurance.

That the decline in life expectancy -- and the immense health problems of poorer parts of the country -- have anything to do with President Trump working hard "to make sure that more Americans lack health insurance" is just ludicrous. Trump does a lot of zany things, but I doubt even Adam Schiff could find a meeting where Trump gets his advisers together to work on "what can we do to make sure some more Americans lack health insurance?" Is this before or after the satanic rituals? The many studies of health insurance don't find much of any correlation with life expectancy. Less stress about medical bills yes.

In fact, Stiglitz goes on to contradict himself.

One reason for declining life expectancy in America is what Anne Case and Nobel laureate economist Angus Deaton call deaths of despair, caused by alcohol, drug overdoses, and suicide. In 2017 (the most recent year for which good data are available), such deaths stood at almost four times their 1999 level. Indeed. And this did not start November 6 2016, nor does it have anything to do with health insurance.
On to money

there was no significant change in the median US household’s disposable income between 2017 and 2018 ...The lion’s share of the increase in GDP is also going to those at the top. Real median weekly earnings are just 2.6% above their level when Trump took office.

Well, "just" 2.6% ain't nothing. And actually there is good news here. John Grigsby has a very nice paper I saw last week pointing out that wages rose in the Great Recession. Why? Well all the low-wage people got fired, so the average wages of those remaining got hired. Right now, we are seeing some of the opposite. People who have been out of the labor force for years are returning. Even ex-cons are getting jobs. Employers are skipping the drugs tests. Hire a lot of people at less than average wages, and average wages go down. It is possible for every individual to get a raise but the average decline.

I'll come back to the economic fortunes of people at the margins in a second, with the CEA's report. Let's finish Stiglitz' doom and gloom.

"Making matters worse, the growth that has occurred is not environmentally sustainable – and even less so thanks to the Trump administration’s gutting of regulations. The air will be less breathable, the water less drinkable, and the planet more subject to climate change. In fact, losses related to climate change have already reached new highs in the US, "

Oh please. In two years? Just how much extra losses has the US suffered because of extra CO2 emitted by the US, and as a result of any Trump deregulatory action? 0.0000000...1? Actually, fracking has led to a natural gas boom which has lowered US emissions more than any other advanced country, no thanks to the Obama era EPA or the states that ban fracking or the candidates that propose to ban it. And has not Tesla grown a lot in the last two years? Where is any evidence that the last two years' growth is any less "sustainable" than before? What does that even mean, other than nasty Trump pulled out of the symbolic Paris accord and won't say nice things about climate? What does that mean to our worker on the low end of the scale, who we were supposed to be worrying about here?

"The tax cuts were supposed to spur a new wave of investment. Instead, they triggered an all-time record binge of share buybacks – some $800 billion in 2018 – by some of America’s most profitable companies,"

That a Nobel Prize winner in economics can spew such obvious nonsense reveals... well, that he's passing on politician's talking points, not economics. (Posts on buybacks here here here) The economy's choice of investment vs. consumption has nothing to do with corporations' decision to buy back shares, pay dividends, buy another company, or invest. If a company buys back shares or pays out a big dividend, that means the company doesn't have any good investment projects. The investors then decide whether to give the money to another company that does have some ideas and is borrowing or issuing shares, or to splurge it on, say a private-jet trip to Davos and some carbon offsets. The overall level of investment depends on their actions, not the repurchase decision. Repurchases were in fact a great reform of a previous era, to empower stockholders to stop companies from "investing" retained earnings in pointless management-aggrandizing projects.

"Likewise, Trump’s trade wars, for all their sound and fury, have not reduced the US trade deficit," 

Well, finally something sensible. I'd go on -- basic economics says the bilateral trade deficit is totally meaningless anyway. Stiglitz seems to be signing on to the opposite, maybe because Democrats are basically with Trump on mercantilism and protection.

Even judging by GDP, the Trump economy falls short. Last quarter’s growth was just 2.1%, far less than the 4%, 5%, or even 6% Trump promised to deliver, and even less than the 2.4% average of Obama’s second term.

I think Obama may have promised a bit better than came true under his watch as well. Comparing one quarter to an 8 year average is an obvious sleight of hand.

And we left behind the supposed despair of the working classes.

Look, you may dislike Trump. You may object to personal style. You may object to trade and foreign policy. You may claim that the economy doesn't have that much to do with a president and deregulation and tax cuts. You may point out how much better things could be. But the claim that things have gotten dramatically worse for those on the bottom end of America's economy; that they were doing great under Obama and fell catastrophically under, or as any result of the actions of this one man, just won't hold. Yes, this is a narrative that some Democratic presidential candidates want to push. Sorry, the Great Depression ended in 1939.

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The CEA reports paint a different picture. On rhetoric, there is a bit more Trump adulation than I'd like, which I think reduces its appeal to the middle and opposite side, just as Stiglitz' Trump vilification turns me off. But the boss pays the bills. 

Annual nominal wage growth reached 3 percent in 2019 for the first time since the Great Recession,

 
This is about the same number as Stiglitz' 2.6% real in two years! This highlights an important point -- I do not see anyone contradicting each other on numbers. Which numbers one looks at, and which insults one hurls (sorry, which causal and motive attributions one makes) seem to be the difference.

More importantly, to the topic -- is growth spreading more or less equally? 

 
wage growth for many historically disadvantaged groups is now higher than wage growth for more advantaged groups, as is the case for lower-income workers compared with higher- income ones, for workers compared with managers, and for African Americans compared with whites.

When measured as the share of income held by the top 20 percent, income inequality fell in 2018 by the largest amount in over a decade

Employment and earnings gains continue pulling people out of poverty and means-tested welfare programs... The number of people living in poverty decreased by 1.4 million from 2017 to 2018, and the poverty rates for blacks and Hispanics reached record lows..

And the lowest wage earners have seen the fastest nominal wage growth (10.6 percent) of any income group since the Tax Cuts and Jobs Act was signed into law. Beyond this pay increase for low-income workers, from the start of the current expansion to December 2016, average wage growth for workers lagged that for managers, and that for African Americans lagged that for white Americans. Since President Trump took office, each of these trends has been reversed, contributing to reduced income inequality.

These employment and income gains have brought people from the sidelines into employment. In the third quarter of 2019, 73.7 percent of workers entering employment came from out of the labor force rather than from unemployment, which is the highest share since the series began in 1990....

a strong market for jobs creates work opportunities for those with less education or training, prior criminal convictions, or a disability. 

Yes, you can argue that it's just a continuation of the previous trend. The CEA would point out that every forecast thought it had to stop, but forecasts have persistently been wrong. But doom and gloom and death and despair is hard to argue.

In September 2019, the unemployment rate for individuals without a high school degree fell to 4.8 percent, achieving a series low (the series began in 1992)

In September 2019, the unemployment rate for persons with a disability dropped to 6.1 percent, the lowest it has been since the series began in 2008.

Now, unemployment rates are not everything. What really matters is the number of people working, and people who aren't even looking are and remain a proble3m.

Although job growth remains robust and the unemployment rate is near a record low, the labor force participation rate has not recovered to its prerecession level.

Stiglitz complained about this. But people in the US are getting older, which skews the numbers towards more people staying out of the labor force.

To find this adjusted rate, the age and sex distribution of the population is first held fixed at a given reference period. The demographically adjusted participation rate for each period is constructed by using that period’s age- and gender-specific participation rates and the population of the reference period.8

The bottom 10 percent wages are going up more than the rest! Now, one may say it's still not enough. The bottom 10 percent have pretty rotten lives, and pretty rotten jobs. But one cannot say it's getting worse.

Minorities are experiencing some of the fastest increases in pay. In 2019:Q3, African Americans saw their weekly earnings grow by 6.0 percent over the year, while Hispanics’ weekly earnings grew by 4.2 percent. For comparison, the 12-month change in weekly earnings for all Americans rose by 3.6 percent.

The (somewhat artificial) poverty line:

In 2018, the official poverty rate fell by 0.5 percentage point, to 11.8 percent, the lowest level since 2001,

Disadvantaged groups experienced the largest poverty reductions in 2018. The poverty rate fell by 0.9 percentage point for black Americans and by 0.8 percentage point for Hispanic Americans, with both groups reaching historic lows (see figure 9). The poverty rates for black and Hispanic Americans in 2018 were never closer to the overall poverty rate in the United States. Children fared especially well in 2018, with a decrease in poverty of 1.2 percentage points for those under 18. Poverty among single mothers with children fell by 2.5 percentage points.

Again, poverty is not fun, and 11.8 percent is more than one would like. But one cannot say it's getting worse.

And they are not done. Among a smorgasbord of proposals, I liked this one

Occupational licensing requirements impose an additional cost on entering a given occupation. There is a wide range of licensed occupations, including plumbers, electricians, florists, and barbers (Meyer 2017). Some occupational licensing restrictions can be justified to protect the public, but the existing requirements for many occupations in many States include jobs that pose no physical or financial risk to the public. Instead, licensing is being used as a barrier to entry into a profession to artificially inflate wages for those already in the profession.

I like it especially because the Obama CEA made a big fuss about this. Here's something we can agree on!

Expanding Opportunities for Ex-Offenders 

Where is Stiglitz cheering?

Since the start of the Trump Administration, supporting working families has been a top priority. In December 2017, the President signed into law the Tax Cuts and Jobs Act, which increased the reward for working by doubling the Child Tax Credit and increasing its refundability. The President signed into law the largest-ever increase in funding for the Child Care and Development Block Grants—expanding access to high-quality child care for nearly 800,000 families across the country. In addition, President Trump was the first president to include nationwide paid parental leave in his annual budget.

Wait I thought that was a tax cut for billionaires. I think this stuff is nutty, but where is Stiglitz cheering?

Opioid deaths

Last year, Congress passed and President Trump signed the SUPPORT Act. The Administration secured $6 billion in new funding in fiscal years 2018 and 2019 to fight opioid abuse and to expand access to medication-assisted treatment. Improved border enforcement may also be reducing the supply of fentanyl; fentanyl seizures by Customs and Border Protection are up 265 percent over the last three fiscal years.


Wait, didn't Stiglitz blame Trump for opioids? Yes, there is a long way to go here, but accusing Trump and co of wanting to "get more Americans hooked" is just not true.

Turning to the "The blue-collar boom reduces inequality"

Net worth held by the bottom 50 percent of households has increased by 47 percent, more than three times the rate of increase for the top 1 percent of households.

The Trump administration is presiding over a great reduction in inequality. Whoda thunk?

I don't personalize things. I don't think Trump had this in mind. He wanted tariffs to bring back manufacturing jobs which they did not do. He did not campaign on "we're going to cut taxes, deregulate, the economy is going to boom, and all the low income low skill people will get jobs." But that is what is happening.

Of course the CEA also jumps quickly to causal conclusions 

Contrary to the narrative that the Trump economy only benefits the top, these facts confirm the United States’ unprecedented blue-collar boom. Increased wealth for the bottom half of American households, faster wage growth for historically disadvantaged Americans, and falling welfare enrollment as incomes rise and people come off the labor market’s sidelines—these are the results of President Trump’s pro-growth, pro-worker policies.

I think we can regard this as at least debatable -- how much is because, how much is despite. That would be an interesting debate with a good left-wing economist. But gloom and doom is simply not true.

Aliens Cause Global Warming: A Caltech Lecture by Michael Crichton

Michael Chrichton puts the Climate Change controversy in perspective.

MC is on target.
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Caltech Michelin Lecture – January 17, 2003

My topic today sounds humorous but unfortunately I am serious. I am going to argue that extraterrestrials lie behind global warming. Or to speak more precisely, I will argue that a belief in extraterrestrials has paved the way, in a progression of steps, to a belief in global warming.

Charting this progression of belief will be my task today. Let me say at once that I have no desire to discourage anyone from believing in either extraterrestrials or global warming. That would be quite impossible to do.

Rather, I want to discuss the history of several widely-publicized beliefs and to point to what I consider an emerging crisis in the whole enterprise of science-namely the increasingly uneasy relationship between hard science and public policy.

I have a special interest in this because of my own upbringing. I was born in the midst of World War II, and passed my formative years at the height of the Cold War. In school drills, I dutifully crawled under my desk in preparation for a nuclear attack.

It was a time of widespread fear and uncertainty, but even as a child I believed that science represented the best and greatest hope for mankind. Even to a child, the contrast was clear between the world of politics-a world of hate and danger, of irrational beliefs and fears, of mass manipulation and disgraceful blots on human history. In contrast, science held different values-international in scope, forging friendships and working relationships across national boundaries and political systems, encouraging a dispassionate habit of thought, and ultimately leading to fresh knowledge and technology that would benefit all mankind.

The world might not be a very good place, but science would make it better. And it did. In my lifetime, science has largely fulfilled its promise. Science has been the great intellectual adventure of our age, and a great hope for our troubled and restless world. But I did not expect science merely to extend lifespan, feed the hungry, cure disease, and shrink the world with jets and cell phones.

I also expected science to banish the evils of human thought—prejudice and superstition, irrational beliefs and false fears. I expected science to be, in Carl Sagan’s memorable phrase, “a candle in a demon haunted world.” And here, I am not so pleased with the impact of science. Rather than serving as a cleansing force, science has in some instances been seduced by the more ancient lures of politics and publicity.

Some of the demons that haunt our world in recent years are invented by scientists. The world has not benefited from permitting these demons to escape free. But let’s look at how it came to pass.

Nir Shaviv lecture on climate change showing the IPCC’s climate change projections are not credible

Here is a link to a talk by Nir Shaviv about climate change.

NS shows what the IPCC climate change attributions leave out and why what they leave out invalidates its conclusions, including the severity of climate change.

It appears that climate change is not an existential threat.

Equilibrium climate sensitivity is nowhere near as high as climate alarmists claim

Judith Curry on equilibrium climate sensitivity and transient climate response.  The climate alarmists' and the IPCC's case for an existential threat is full of holes.

Let's face it - do you think Al Gore, Greta Thunberg and their ilk understand any of this?

It amazes me that so many are so confident of an alleged existential threat that they have no basis for understanding the arguments for or against.

Not even the kind of statistical models used here are good proxies for the physics of climate change.  Consider, for example, whether they can plausibly reflect periodic ice ages. I suspect arima models are not up to the task except to approximate relatively short periods in a curve fitting sense.  Curve fitting is not physics.
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Challenging the claim that a large set of climate model runs published since 1970’s are consistent with observations for the right reasons.

Introduction

Zeke Hausfather et al. (2019) (herein ZH19) examined a large set of climate model runs published since the 1970s and claimed they were consistent with observations, once errors in the emission projections are considered. It is an interesting and valuable paper and has received a lot of press attention. In this post, I will explain what the authors did and then discuss a couple of issues arising, beginning with IPCC over-estimation of CO2 emissions, a literature to which Hausfather et al. make a striking contribution. I will then present a critique of some aspects of their regression analyses. I find that they have not specified their main regression correctly, and this undermines some of their conclusions. Using a more valid regression model helps explain why their findings aren’t inconsistent with Lewis and Curry (2018) which did show models to be inconsistent with observations.

Outline of the ZH19 Analysis:

A climate model projection can be factored into two parts: the implied (transient) climate sensitivity (to increased forcing) over the projection period and the projected increase in forcing. The first derives from the model’s Equilibrium Climate Sensitivity (ECS) and the ocean heat uptake rate. It will be approximately equal to the model’s transient climate response (TCR), although the discussion in ZH19 is for a shorter period than the 70 years used for TCR computation. The second comes from a submodel that takes annual GHG emissions and other anthropogenic factors as inputs, generates implied CO2 and other GHG concentrations, then converts them into forcings, expressed in Watts per square meter. The emission forecasts are based on socioeconomic projections and are therefore external to the climate model.

ZH19 ask whether climate models have overstated warming once we adjust for errors in the second factor due to faulty emission projections. So it’s essentially a study of climate model sensitivities. Their conclusion, that models by and large generate accurate forcing-adjusted forecasts, implies that models have generally had valid TCR levels. But this conflicts with other evidence (such as Lewis and Curry 2018) that CMIP5 models have overly high TCR values compared to observationally-constrained estimates. This discrepancy needs explanation.

One interesting contribution of the ZH19 paper is their tabulation of the 1970s-era climate model ECS values. The wording in the ZH19 Supplement, which presumably reflects that in the underlying papers, doesn’t distinguish between ECS and TCR in these early models. The reported early ECS values are:

• Manabe and Weatherald (1967) / Manabe (1970) / Mitchell (1970): 2.3K
• Benson (1970) / Sawyer (1972) / Broecker (1975): 2.4K
• Rasool and Schneider (1971) 0.8K
• Nordhaus (1977): 2.0K

If these really are ECS values they are pretty low by modern standards. It is widely-known that the 1979 Charney Report proposed a best-estimate range for ECS of 1.5—4.5K. The follow-up National Academy report in 1983 by Nierenberg et al. noted (p. 2) “The climate record of the past hundred years and our estimates of CO2 changes over that period suggest that values in the lower half of this range are more probable.” So those numbers might be indicative of general thinking in the 1970s. Hansen’s 1981 model considered a range of possible ECS values from 1.2K to 3.5K, settling on 2.8K for their preferred estimate, thus presaging the subsequent use of generally higher ECS values.

But it is not easy to tell if these are meant to be ECS or TCR values. The latter are always lower than ECS, due to slow adjustment by the oceans. Model TCR values in the 2.0–2.4 K range would correspond to ECS values in the upper half of the Charney range.

If the models have high interval ECS values, the fact that ZH19 find they stay in the ballpark of observed surface average warming, once adjusted for forcing errors, suggests it’s a case of being right for the wrong reason. The 1970s were unusually cold, and there is evidence that multidecadal internal variability was a significant contributor to accelerated warming from the late 1970s to the 2008 (see DelSole et al. 2011). If the models didn’t account for that, instead attributing everything to CO2 warming, it would require excessively high ECS to yield a match to observations.

With those preliminary points in mind, here are my comments on ZH19.

There are some math errors in the writeup.

The main text of the paper describes the methodology only in general terms. The online SI provides statistical details including some mathematical equations. Unfortunately, they are incorrect and contradictory in places. Also, the written methodology doesn’t seem to match the online Python code. I don’t think any important results hang on these problems, but it means reading and replication is unnecessarily difficult. I wrote Zeke about these issues before Christmas and he has promised to make any necessary corrections to the writeup.

One of the most remarkable findings of this study is buried in the online appendix as Figure S4, showing past projection ranges for CO2 concentrations versus observations:

Bear in mind that, since there have been few emission reduction policies in place historically (and none currently that bind at the global level), the heavy black line is effectively the Business-as-Usual sequence. Yet the IPCC repeatedly refers to its high end projections as “Business-as-Usual” and the low end as policy-constrained. The reality is the high end is fictional exaggerated nonsense.

I think this graph should have been in the main body of the paper. It shows:

  

In the 1970s, models (blue) had a wide spread but on average encompassed the observations (though they pass through the lower half of the spread);

• In the 1980s there was still a wide spread but now the observations hug the bottom of it, except for the horizontal line which was Hansen’s 1988 Scenario C;
• Since the 1990s the IPCC constantly overstated emission paths and, even more so, CO2 concentrations by presenting a range of future scenarios, only the minimum of which was ever realistic.

I first got interested in the problem of exaggerated IPCC emission forecasts in 2002 when the top-end of the IPCC warming projections jumped from about 3.5 degrees in the 1995 SAR to 6 degrees in the 2001 TAR. I wrote an op-ed in the National Post and the Fraser Forum (both available here) which explained that this change did not result from a change in climate model behaviour but from the use of the new high-end SRES scenarios, and that many climate modelers and economists considered them unrealistic. The particularly egregious A1FI scenario was inserted into the mix near the end of the IPCC process in response to government (not academic) reviewer demands. IPCC Vice-Chair Martin Manning distanced himself from it at the time in a widely-circulated email, stating that many of his colleagues viewed it as “unrealistically high.”

Some longstanding readers of Climate Etc. may also recall the Castles-Henderson critique which came out at this time. It focused on IPCC misuse of Purchasing Power Parity aggregation rules across countries. The effect of the error was to exaggerate the relative income differences between rich and poor countries, leading to inflated upper end growth assumptions for poor countries to converge on rich ones. Terence Corcoran of the National Post published an article on November 27 2002 quoting John Reilly, an economist at MIT, who had examined the IPCC scenario methodology and concluded it was “in my view, a kind of insult to science” and the method was “lunacy.”

Years later (2012-13) I published two academic articles (available here) in economics journals critiquing the IPCC SRES scenarios. Although global total CO2 emissions have grown quite a bit since 1970, little of this is due to increased average per capita emissions (which have only grown from about 1.0 to 1.4 tonnes C per person), instead it is mainly driven by global population growth, which is slowing down. The high-end IPCC scenarios were based on assumptions that population and per capita emissions would both grow rapidly, the latter reaching 2 tonnes per capita by 2020 and over 3 tonnes per capita by 2050. We showed that the upper half of the SRES distribution was statistically very improbable because it would require sudden and sustained increases in per capita emissions which were inconsistent with observed trends. In a follow-up article, my student Joel Wood and I showed that the high scenarios were inconsistent with the way global energy markets constrain hydrocarbon consumption growth. More recently Justin Ritchie and Hadi Dowladabadi have explored the issue from a different angle, namely the technical and geological constraints that prevent coal use from growing in the way assumed by the IPCC (see here and here).

IPCC reliance on exaggerated scenarios is back in the news, thanks to Roger Pielke Jr.’s recent column on the subject (along with numerous tweets from him attacking the existence and usage of RCP8.5) and another recent piece by Andrew Montford. What is especially egregious is that many authors are using the top end of the scenario range as “business-as-usual”, even after, as shown in the ZH19 graph, we have had 30 years in which business-as-usual has tracked the bottom end of the range.

In December 2019 I submitted my review comments for the IPCC AR6 WG2 chapters. Many draft passages in AR6 continue to refer to RCP8.5 as the BAU outcome. This is, as has been said before, lunacy—another “insult to science”.

Apples-to-apples trend comparisons requires removal of Pinatubo and ENSO effects

The model-observational comparisons of primary interest are the relatively modern ones, namely scenarios A—C in Hansen (1988) and the central projections from various IPCC reports: FAR (1990), SAR (1995), TAR (2001), AR4 (2007) and AR5 (2013). Since the comparison uses annual averages in the out-of-sample interval the latter two time spans are too short to yield meaningful comparisons.

Before examining the implied sensitivity scores, ZH19 present simple trend comparisons. In many cases they work with a range of temperatures and forcings but I will focus on the central (or “Best”) values to keep this discussion brief.

ZH19 find that Hansen 1988-A and 1988-B significantly overstate trends, but not the others. However, I find FAR does as well. SAR and TAR don’t but their forecast trends are very low.

The main forecast interval of interest is from 1988 to 2017. It is shorter for the later IPCC reports since the start year advances. To make trend comparisons meaningful, for the purpose of the Hansen (1988-2017) and FAR (1990-2017) interval comparisons, the 1992 (Mount Pinatubo) event needs to be removed since it depressed observed temperatures but is not simulated in climate models on a forecast basis. Likewise with El Nino events. By not removing these events the observed trend is overstated for the purpose of comparison with models.

To adjust for this I took the Cowtan-Way temperature series from the ZH19 data archive, which for simplicity I will use as the lone observational series, and filtered out volcanic and El Nino effects as follows. I took the IPCC AR5 volcanic forcing series (as updated by Nic Lewis for Lewis&Curry 2018), and the NCEP pressure-based ENSO index (from here). I regressed Cowtan-Way on these two series and obtained the residuals, which I denote as “Cowtan-Way adj” in the following Figure (note both series are shifted to begin at 0.0 in 1988):

The trends, in K/decade, are indicated in the legend. The two trend coefficients are not significantly different from each other (using the Vogelsang-Franses test). Removing the volcanic forcing and El Nino effects causes the trend to drop from 0.20 to 0.15 K/decade. The effect is minimal on intervals that start after 1995. In the SAR subsample (1995-2017) the trend remains unchanged at 0.19 K/decade and in the TAR subsample (2001-2017) the trend increases from 0.17 to 0.18 K/decade.

Here is what the adjusted Cowtan-Way data looks like, compared to the Hansen 1988 series:

The linear trend in the red line (adjusted observations) is 0.15 C/decade, just a bit above H88-C (0.12 C/decade) but well below the H88-A and H88-B trends (0.30 and 0.28 C/decade respectively)

The ZH19 trend comparison methodology is an ad hoc mix of OLS and AR1 estimation. Since the methodology write-up is incoherent and their method is non-standard I won’t try to replicate their confidence intervals (my OLS trend coefficients match theirs however). Instead I’ll use the Vogelsang-Franses (VF) autocorrelation-robust trend comparison methodology from the econometrics literature. I computed trends and 95% CI’s in the two CW series, the 3 Hansen 1988 A,B,C series and the first three IPCC out-of-sample series (denoted FAR, SAR and TAR). The results are as follows:

The OLS trends (in K/decade) are in the 1st column and the lower and upper bounds on the 95% confidence intervals are in the next two columns.

The 4th and 5th columns report VF test scores, for which the 95% critical value is 41.53. In the first two rows, the diagonal entries (906.307 and 348.384) are tests on a null hypothesis of no trend; both reject at extremely small significance levels (indicating the trends are significant). The off-diagonal scores (21.056) test if the trends in the raw and adjusted series are significantly different. It does not reject at 5%.

The entries in the subsequent rows test if the trend in that row (e.g. H88-A) equals the trend in, respectively, the raw and adjusted series (i.e. obs and obs2), after adjusting the sample to have identical time spans. If the score exceeds 41.53 the test rejects, meaning the trends are significantly different.

The Hansen 1988-A trend forecast significantly exceeds that in both the raw and adjusted observed series. The Hansen 1988-B forecast trend does not significantly exceed that in the raw CW series but it does significantly exceed that in the adjusted CW (since the VF score rises to 116.944, which exceeds the 95% critical value of 41.53). The Hansen 1988-C forecast is not significantly different from either observed series. Hence, the only Hansen 1988 forecast that matches the observed trend, once the volcanic and El Nino effects are removed, is scenario C, which assumes no increase in forcing after 2000. The post-1998 slowdown in observed warming ends up matching a model scenario in which no increase in forcing occurs, but does not match either scenario in which forcing is allowed to increase, which is interesting.

The forecast trends in FAR and SAR are not significantly different from the raw Cowtan-Way trends but they do differ from the adjusted Cowtan-Way trends. (The FAR trend also rejects against the raw series if we use GISTEMP, HadCRUT4 or NOAA). The discrepancy between FAR and observations is due to the projected trend being too large. In the SAR case, the projected trend is smaller than the observed trend over the same interval (0.13 versus 0.19). The adjusted trend is the same as the raw trend but the series has less variance, which is why the VF score increases. In the case of CW and Berkeley it rises enough to reject the trend equivalence null; if we use GISTEMP, HadCRUT4 or NOAA neither raw nor adjusted trends reject against the SAR trend.

The TAR forecast for 2001-2017 (0.167 K/decade) never rejects against observations.

So to summarize, ZH19 go through the exercise of comparing forecast to observed trends and, for the Hansen 1988 and IPCC trends, most forecasts do not significantly differ from observations. But some of that apparent fit is due to the 1992 Mount Pinatubo eruption and the sequence of El Nino events. Removing those, the Hansen 1988-A and B projections significantly exceed observations while the Hansen 1988 C scenario does not. The IPCC FAR forecast significantly overshoots observations and the IPCC SAR significantly undershoots them.

In order to refine the model-observation comparison it is also essential to adjust for errors in forcing, which is the next task ZH19 undertake.

Implied TCR regressions: a specification challenge

ZH19 define an implied Transient Climate Response (TCR) as

where T is temperature, F is anthropogenic forcing, and the derivative is computed as the least squares slope coefficient from regressing temperature on forcing over time. Suppressing the constant term the regression for model i is simply

The TCR for model i is therefore where 3.7 (W/m2) is the assumed equilibrium CO2 doubling coefficient. They find 14 of the 17 implied TCR’s are consistent with an observational counterpart, defined as the slope coefficient from regressing temperatures on an observationally-constrained forcing series.

Regarding the post-1988 cohort, unfortunately ZH19 relied on an ARIMA(1,0,0) regression specification, or in other words a linear regression with AR1 errors. While the temperature series they use are mostly trend stationary (i.e. stationary after de-trending), their forcing series are not. They are what we call in econometrics integrated of order 1, or I(1), namely the first differences are trend stationary but the levels are nonstationary. I will present a very brief discussion of this but I will save the longer version for a journal article (or a formal comment on ZH19).

There is a large and growing literature in econometrics journals on this issue as it applies to climate data, with lots of competing results to wade through. On the time spans of the ZH19 data sets, the standard tests I ran (namely Augmented Dickey-Fuller) indicate temperatures are trend-stationary while forcings are nonstationary. Temperatures therefore cannot be a simple linear function of forcings, otherwise they would inherit the I(1) structure of the forcing variables. Using an I(1) variable in a linear regression without modeling the nonstationary component properly can yield spurious results. Consequently it is a misspecification to regress temperatures on forcings (see Section 4.3 in this chapter for a partial explanation of why this is so).

How should such a regression be done? Some time series analysts are trying to resolve this dilemma by claiming that temperatures are I(1). I can’t replicate this finding on any data set I’ve seen, but if it turns out to be true it has massive implications including rendering most forms of trend estimation and analysis hitherto meaningless.

I think it is more likely that temperatures are I(0), as are natural forcings, and anthropogenic forcings are I(1). But this creates a big problem for time series attribution modeling. It means you can’t regress temperature on forcings the way ZH19 did; in fact it’s not obvious what the correct way would be. One possible way to proceed is called the Toda-Yamamoto method, but it is only usable when the lags of the explanatory variable can be included, and in this case they can’t because they are perfectly collinear with each other. The main other option is to regress the first differences of temperatures on first differences of forcings, so I(0) variables are on both sides of the equation. This would imply an ARIMA(0,1,0) specification rather than ARIMA(1,0,0).

But this wipes out a lot of information in the data. I did this for the later models in ZH19, regressing each one’s temperature series on each one’s forcing input series, using a regression of Cowtan-Way on the IPCC total anthropogenic forcing series as an observational counterpart. Using an ARIMA(0,1,0) specification except for AR4 (for which ARIMA(1,0,0) is indicated) yields the following TCR estimates:

The comparison of interest is OBS1 and OBS2 to the H88a—c results, and for each IPCC report the OBS-(startyear) series compared to the corresponding model-based value. I used the unadjusted Cowtan-Way series as the observational counterparts for FAR and after.

In one sense I reproduce the ZH19 findings that the model TCR estimates don’t significantly differ from observed, because of the overlapping spans of the 95% confidence intervals. But that’s not very meaningful since the 95% observational CI’s also encompass 0, negative values, and implausibly high values. They also encompass the Lewis & Curry (2018) results. Essentially, what the results show is that these data series are too short and unstable to provide valid estimates of TCR. The real difference between models and observations is that the IPCC models are too stable and constrained. The Hansen 1988 results actually show a more realistic uncertainty profile, but the TCR’s differ a lot among the three of them (point estimates 1.5, 1.9 and 2.4 respectively) and for two of the three they are statistically insignificant. And of course they overshoot the observed warming.

The appearance of precise TCR estimates in ZH19 is spurious due to their use of ARIMA(1,0,0) with a nonstationary explanatory variable. A problem with my approach here is that the ARIMA(0,1,0) specification doesn’t make efficient use of information in the data about potential long run or lagged effects between forcings and temperatures, if they are present. But with such short data samples it is not possible to estimate more complex models, and the I(0)/I(1) mismatch between forcings and temperatures rule out finding a simple way of doing the estimation.

Conclusion

The apparent inconsistency between ZH19 and studies like Lewis & Curry 2018 that have found observationally-constrained ECS to be low compared to modeled values disappears once the regression specification issue is addressed. The ZH19 data samples are too short to provide valid TCR values and their regression model is specified in such a way that it is susceptible to spurious precision. So I don’t think their paper is informative as an exercize in climate model evaluation.

It is, however, informative with regards to past IPCC emission/concentration projections and shows that the IPCC has for a long time been relying on exaggerated forecasts of global greenhouse gas emissions.

I’m grateful to Nic Lewis for his comments on an earlier draft.

Comment from Nic Lewis

These early models only allowed for increases in forcing from CO2, not from all forcing agents. Since 1970, total forcing (per IPCC AR5 estimates) has grown more than 50% faster than CO2-only forcing, so if early model temperature trends and CO2 concentration trends over their projection periods are in line with observed warming and CO2 concentration trends, their TCR values must have been more than 50% above that implied by observations.

Europe: An Example of where Green extremism leads

Victor Davis Hanson at townhall.com.

Does the fact that the US produces more energy than it uses imply that losing production elsewhere has no impact on the US?  No, with world markets, any reduction in supply will increase price.  On the other hand, a given reduction in supply elsewhere no longer will increase price by as much as it would if the US produced less energy.
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Despite its cool Green parties and ambitious wind and solar agendas, Europe remains by far the world's largest importer of oil and natural gas.

Oil output in the North Sea and off the coast of Norway is declining, and the European Union is quietly looking for fossil fuel energy anywhere it can find it.

Europe itself is naturally rich in fossil fuels. It likely has more reserves of shale gas than the United States, currently the world's largest producer of both oil and natural gas. Yet in most European countries, horizontal drilling and fracking to extract gas and oil are either illegal or face so many court challenges and popular protests that they are neither culturally nor economically feasible.

The result is that Europe is almost entirely dependent on Russian, Middle Eastern and African sources of energy.

The American-Iranian standoff in the Middle East, coupled with radical drop-offs in Iranian and Venezuelan oil production, has terrified Europe -- and for understandable reasons.

The European Union has almost no ability to guarantee the delivery of critical oil and gas supplies from the Middle East should Iran close the Strait of Hormuz or harass ships in the Persian Gulf.

Europe's only maritime security is the NATO fleet -- a synonym for the U.S. Navy.

Vladimir Putin's Russia supplies an estimated 30 percent of Europe's oil needs. In times of crisis, Putin could exercise de facto control over the European economy.

In other words, Europe refuses to develop its own gas and oil reserves, and won't fund the necessary military power to ensure that it can safely import energy from problematic or even hostile sources.

It's no wonder that Europe's traditional foreign policy reflects these crazy paradoxes.

Energy neediness explains why the EU was so eager to maintain the so-called "Iran deal" with the theocracy in Tehran, and also why it was nervous about the anti-Russia hysteria that arose in the United States after the 2016 election.

Past European distancing from Israel reflected Europe's fear of alienating Arab oil producers in the Middle East and North Africa.

Europeans are also uneasy about the Trump administration. They see the current U.S. government as nationalist and unpredictable. Americans appear not so ready as in the past to enter the world's hotspots to ensure unimpeded commercial use of sea and air lanes for the benefit of others.

The result is a sort of European schizophrenia when it comes to America and foreign policy in general. On one hand, the European Union resents its military dependence on Washington, while on the other it prays for its continuance. The EU loudly promotes freedom and democracy abroad, but it is careful to keep ties with oil-exporting Middle Eastern autocracies that are antithetical to every value Europeans promote.

Germany agrees with its allies that Russian imperial agendas could threaten European autonomy. But privately, Berlin reassures Putin's Russia that it wants to buy all the gas and oil that Moscow has to sell. Germany increasingly seems far friendlier with a suspicious Russia than it is with an America that protects it.

In sum, what ensures that Europeans have enough daily gasoline and home heating fuel are not batteries, wind farms and solar panels -- much less loud green proselytizing. They count instead on a mercurial Russia, an array of unstable Middle Eastern governments and an underappreciated U.S. military.

In a logical world, Europeans would retake control of their own destiny. That recalibration would entail beefing up their military power, and their navies in particular.

They also would begin to frack and horizontally drill. Europeans would push ahead with more nuclear power, hydroelectric projects and clean-coal technologies -- at least until new sources of clean energies become viable.

Europe should applaud U.S. gas and oil development, which has upped world supplies, diversified suppliers and lowered global prices. Europeans should especially remember that the U.S. military keeps global commerce safe for all vulnerable importers such as themselves.

But these remedies are apparently seen in Europe as worse than the disease of oil and gas dependency.

The result is again chaos. Europe lectures about greenhouse gases while it desperately seeks supplies of fossil fuels. Germany usually sets the tone in Europe, and it is the most hypocritical in both denouncing and buying fossil fuels from unsavory sources.

The danger for Europe now is that the charade may soon be over.

Americans are self-sufficient in gas and oil. They have lost interest in Middle East quagmires and petro-regimes. And they don't like patrolling the world for countries that both count on and ankle-bite the U.S. military. Meanwhile, the more Europeans pander to oil-rich Russia, Iran and various Gulf states, the less respect they earn in return.

It is hard to be both the world largest importer of gas and oil and the loudest critic of fossil fuels, but Europe has managed to do it.

Stochastic Portfolio Theory and the Low Beta Anomaly

Here is a link to the paper.

Does Roundup cause cancer? The other side of the story

Paul Driessen at townhall.com

Claims by lawyers and court judgments cannot be accepted at face value.  As one lawyer told me, "The court is a place to lie".  Moreover, judges do not have the knowledge to know when science and statistics is being misrepresented to them.

Here is the article.
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The US Environmental Protection Agency (EPA) recently issued a finding that could – and certainly should – undermine some of the most outrageous lawsuits and jury awards in American history.

Bolstered by San Francisco area juries that have given multi-multi-million-dollar awards to clients who claim glyphosate (the active ingredient in Roundup weedkiller) caused their cancer, jackpot justice lawyers have recruited some 20,000 additional “corporate victims” who hope to reap their own fortunes.

Their cases are based on the assertion that: (a) Bayer-Monsanto negligently or deliberately failed to warn consumers that the glyphosate it manufactures is carcinogenic; (b) the plaintiffs used Roundup at some point in their lives; and (c) their short or long-term use of the chemical caused their Non-Hodgkin Lymphoma or other cancer. Those claims are dependent on several essential factors.

First and foremost, a 2015 determination by the France-based International Agency for Cancer Research (IARC) that glyphosate is a Group 2A probable human carcinogen. Second, a 2017 decision by the State of California to add the chemical to its Safe Drinking Water and Toxic Enforcement Act (Prop 65) official list of carcinogens, based on the IARC decision. Third, the state’s requirement that all Roundup labels must therefore carry prominent warnings that the product “probably” causes cancer.

Monsanto and Bayer insist that their product is safe and non-carcinogenic; Roundup labels thus did not carry warnings. But that gave plaintiff lawyers the opportunity to argue in pleadings, courtroom statements and media ads that the company negligently or deliberately caused serious health risks.

In the minds of presiding judges and jurors, if it was “possible” that even short or occasional exposure to Roundup could have caused cancer – even if it was an extremely remote likelihood – the manufacturer was guilty, and liable. Hence, awards in the tens of millions or even billions of dollars were justified.

There are numerous fundamental, even monumental, problems with this strained reasoning – and they are likely to be exacerbated by the August 7, 2019 EPA decision and strongly worded guidance letter.

IARC is virtually the only organization in the world to conclude that glyphosate is carcinogenic – and it based its conclusions on examining just eight studies. Far worse, subsequent reviews by epidemiologist Dr. Geoffrey Kabat, National Cancer Institute statistician Dr. Robert Tarone, investigative journalist Kate Kelland and “RiskMonger” Dr. David Zaruk demonstrated that the IARC decision resulted from bias, improper revision of study data and/or results, and collusion between glyphosate trial lawyers and the IARC consultant who led the agency’s investigation and was paid handsomely by the trial lawyers.

Equally outrageous and illuminating, IARC classifies red meat, very hot beverages, emissions from frying food, even doing shift work as “probable” human carcinogens – in the same category as glyphosate. It lists pickled vegetables and caffeic acid in coffee, tea and broccoli as “possible” human carcinogens. It even admitted that its glyphosate decision was based on only “limited” evidence of cancer in humans and “sufficient” evidence of cancer in experimental animals. IARC seems to say everything causes cancer.

Perhaps that is because, to reach its conclusions, the agency relies on what toxicity experts call “exposure” or “hazard” tests. That antiquated approach uses lab animals to determine whether a chemical might cause cancer – even if only at ridiculously high levels that no animal or human would ever be exposed to in real life. It refuses to rely on the modern approach of assessing actual risk, by determining the exposure level at which a substance might actually have an adverse effect on animals or humans.

And yet the judges in these cases let the plaintiff lawyers focus on IARC’s claims of carcinogenicity, while they prevented defense attorneys from countering IARC cancer claims or discussing its gross misconduct. They even barred the presentation of extensive evidence that glyphosate is not carcinogenic.

In fact, glyphosate has been used safely since 1974. It is now licensed in 130 countries for more than 100 food crops. Over the past four decades, respected agencies and organizations worldwide have conducted over 3,300 studies, and every one of them concluded that glyphosate is safe and non-carcinogenic.

Reviewers include the European Food Safety Authority, European Chemicals Agency, UN Food and Agriculture Organization, Germany’s Institute for Risk Assessment, Australia’s Pesticides and Veterinary Medicines Authority, Japanese and New Zealand agencies, and the US Environmental Protection Agency. “No pesticide regulatory authority in the world considers glyphosate to be a cancer risk to humans at the levels at which humans are currently exposed,” Health Canada noted. Meanwhile, the National Cancer Institute’s ongoing Agricultural Health Study has evaluated 54,000 farmers and commercial pesticide applicators for over two decades – and likewise found no glyphosate-cancer link.

Amid all of this, the “cancer victim” patients and their lawyers benefitted immensely from endless print, radio, television, online and social media campaigns that have misinformed, pressured, harassed and intimidated prospective judges and jurors. Many of these campaigns and several “educational think tanks” are funded, directly or indirectly, by the predatory tort lawyers and their anti-chemical activist allies.

To top it off, the judges and tort lawyers have made it difficult or impossible for Bayer-Monsanto attorneys to present other highly relevant evidence: such as plaintiffs’ family cancer history and personal dietary and other lifestyle choices – and their exposure to scores of other definite, probable and possible carcinogens on IARC’s list of hundreds of human cancer risks, including those mentioned above.

The supposed corporate cancer victims were allowed to argue that, despite all these other factors, including multiple other carcinogen exposures, their cancer was due solely to their exposure to glyphosate.

Enter EPA. The agency had already conducted lengthy and extensive reviews of the global compendium of studies and regulatory decisions on glyphosate – and had likewise concluded that “glyphosate is not likely to be carcinogenic in humans.” But at least one judge blocked the introduction of the EPA analyses, claiming “the primary inquiry is what the scientific studies show, not what the EPA concluded they show.” He didn’t seem to mind that IARC doesn’t do original studies either – and its ruling on glyphosate was based on what IARC concluded eight studies showed, while ignoring 3,300 contradictory studies.

It will henceforth be much harder for tort lawyers and trial judges to pull that cute little tactic off again. As noted above, EPA has issued a guidance letter – based on (a) its careful “independent evaluation” and reexamination of scientific studies and regulatory determinations around the world; and (b) its regulatory and labeling authority under the Federal Insecticide, Fungicide and Rodenticide Act (FIFRA).

Not only does EPA “disagree with IARC’s assessment of glyphosate.” It concludes that the chemical “is not likely to be carcinogenic to humans.” Equally important, based on its findings, EPA now holds that any “Proposition 65 warning language” based on claims that glyphosate is carcinogenic “constitute[s] a false and misleading statement.” Any products bearing Prop 65 warning statements due to the presence of glyphosate in them are thus “misbranded.” EPA will no longer approve such labels, and any such warnings “must be removed from all product labels where the only basis for the warning is glyphosate.

Applying that decision to these lawsuits, because glyphosate is not carcinogenic, Bayer-Monsanto was and is under no obligation to put warning labels on Roundup containers, stating that the chemical causes or “probably” causes cancer in humans. In fact, the company is legally obligated not to issue such warnings, because they would make the label “false and misleading.”

There is therefore no basis for cancer claims based on IARC’s erroneous, sloppy, collusive, even fraudulent “science.” Thus there is no legal or scientific basis for these lawsuits and jury awards.

It’s time for trial and appellate court judges – and state and federal regulatory authorities – to implement these EPA findings in courtrooms, in news and activist website statements, and in the ubiquitous ads that are trolling for still more Roundup-glyphosate “victims” and predatory tort lawyer clients.

A wealth tax is a bad idea

David Henderson at hoover.org explains why a wealth tax is a bad idea.

DH provides the perspective you need to understand why those advocating a wealth tax either don't understand the consequences, are disingenuous, or are trying to take advantage of your greed.

Some people believe that the existence of substantial wealth inequality implies unfair advantages by the wealthy. Here is an interesting fact that should make you think twice about that.

Suppose that everyone starts with the same wealth and that each person's wealth grows randomly each year with the random changes having the same mean return (continuous) and standard deviation.  Then the result over time is more and more wealth inequality.  The common view that wealth inequality is necessarily due to relative advantage is untrue.
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There’s something happening here.

What it is ain’t exactly clear.

There’s a man with a gun over there,

Telling me I’ve got to beware.

--from Buffalo Springfield, “For What It’s Worth,” 1966.

During the last few years, and especially this year, we have seen a growing attack on the very wealthy and even, to some extent, the very idea of wealth.

In September, for example, Sen. Bernie Sanders, a leading candidate for the 2020 Democratic nomination for President, stated “I don’t think that billionaires should exist in the United States,” adding “I hope the day comes when they don’t.” He also referred to the current income and wealth inequality in the United States as “outrageous and immoral.” His fellow Democratic candidate, Sen. Elizabeth Warren, whose net worth is $12 million, has also been hostile to the very wealthy. Both she and Bernie advocate a substantial annual tax on wealth. Warren proposes a 2 percent annual tax on all wealth over $50 million and a 6 percent annual tax on all wealth over $1 billion. Sanders proposes a much higher tax on wealth, starting at 1 percent on wealth above $32 million and reaching 8 percent on wealth over $10 billion.

It’s not just Bernie and Warren. Even some prominent economists advocate substantial taxes on wealth. So, is such a tax justified? Will it have good economic effects? The answers: No, and no. It’s wrong to take people’s wealth when they have earned it or even inherited it. It’s theirs. And a tax on wealth would discourage people from building wealth and encourage the already wealthy to use their wealth in less-productive ways, making the rest of us a little poorer than otherwise. So both on grounds of fairness and economic well-being generally, a tax on wealth is a bad idea.

Fortunately, it’s not just free-market economists like me who believe this. One of the strongest opponents of a wealth tax, who bases his opposition totally on the economic effects of such a tax, is former Treasury Secretary Lawrence H. Summers.

Before we consider the economic effects, let’s take a minute to ponder the philosophical case for and against a tax on wealth. One case for is that the wealthy got their wealth by plunder. “Behind every great fortune lies a great crime,” said French novelist Honoré de Balzac. His implication was not just that wealthy people have committed crimes. In his book Three Felonies a Day: How the Feds Target the Innocent, criminal defense attorney Harvey Silverglate argues loosely that a large percent of American adults are criminals even if they don’t know it. Silverglate’s book focuses on crimes that businessmen can commit in their daily business, and virtually every fortune comes out of running or owning a business. But Balzac wasn’t talking about the penny-ante crimes Silverglate documents that can get people in legal trouble; that’s why Balzac used the adjective “great” to describe the crime.

What if we accept Balzac’s claim as true. I don’t accept it, and I’ll say why anon, but let’s entertain the idea for a minute.

What follows from that? Wouldn’t the best strategy be to charge the criminals with their crimes? In one of her campaign ads, Warren highlights billionaire Leon Cooperman, who she claims was charged with insider trading. Put aside the debate over whether insider trading should be illegal. (For a statement about why it shouldn’t be illegal, see this.) Notice two things. First, Warren says that Cooperman was charged with insider trading. But his firm, Omega Advisers, settled with the Securities and Exchange Commission, paying a fine of $4.9 million, and admitted no wrongdoing. Did the firm engage in insider trading? I don’t know. And neither does Warren. But if the SEC had been fairly confident that it could win the case, it didn’t have to settle.

As noted above, I don’t accept that behind every fortune, or even most fortunes, is a great crime. Interestingly also, neither does the main economist who got the ball rolling on wealth taxes earlier this decade. The economist who, more than any other, made attacks on the wealthy more generally respected, is Frenchman Thomas Piketty. His 2014 best seller, Capital in the Twenty-First Century, which, incidentally, made him a wealthy man—by January 2015, it had sold 1.5 million copies—gave a sustained argument for heavy taxes on wealth. But even Piketty admits that one can acquire a huge fortune without committing a crime.

Piketty writes, “To be frank, I know virtually nothing about exactly how Carlos Slim [the richest man in Mexico] or Bill Gates became rich, and I am quite incapable of assessing their relative merits.” Translation: even if they didn’t commit crimes, the government should take a substantial portion of their wealth. Addressing the possible relationship between crime and wealth, Piketty continues, “In any case, the courts cannot resolve every case of ill-gotten gains or unjustified wealth. A tax on capital would be a less blunt and more systematic instrument for dealing with the question.” Excuse me? A tax on capital is less blunt than using the legal system to go after those who have committed crimes? That makes no sense. If the goal is to go after ill-gotten gains or unjustified wealth, a tax on capital, i.e., wealth, is a completely blunt instrument.

Let’s say you don’t buy my philosophical reasoning about why people who create wealth deserve it. There’s still a strong economic case for not taxing wealth. Allowing people to keep their wealth gives them an incentive to save and invest in capital. The greater the amount of capital, the more capital there is for workers to use on the job. Remember that capital is not money; capital is made up of things like plant and equipment. Even a sewing machine is valuable capital if the alternative is sewing by hand. The greater the amount of capital per worker, the higher is the productivity of workers. And the higher the productivity of workers, the higher are real wages. Think about the productivity of a woman in Guatemala who has a sewing machine versus one who doesn’t. A tax on capital would cause capital to grow more slowly and, therefore, would cause real wages to grow more slowly. Which would you rather have: Bill Gates having built a company that generates products that make virtually all of us more productive, or Bill Gates, early in the 1980s, deciding not to grow Microsoft and, instead, taking his millions and buying a nice house? I’m glad he chose the first option. I wouldn’t be writing this article on a computer if neither he nor others had bothered to innovate.

You might think that Gates and Microsoft captured most of the gains from innovating for themselves. Even if they had, we would still be better off as long as we consumers got a sliver of the gains. It turns out, though, that the innovators are the people who get only a sliver. In a pathbreaking study in 2004, Yale University economist William D. Nordhaus, who was co-winner of the Nobel Prize in economics in 2018, estimated that between 1948 and 2001, the vast majority of the gains from innovation were “passed on to consumers rather than captured by producers.” Specifically, he wrote, “2.2 percent of the total present value of social returns to innovation are captured by innovators.” Maybe we should change the Balzac saying to make it more on target economically. How about “Behind every great gain to consumers is an innovator.”

One economist who, surprisingly and disappointingly, has said positive things about taxing the wealthy more heavily is MIT’s Robert Solow. He won the Nobel Prize in economics in 1987 for his work on explaining sources of economic growth. In his model, two important sources are capital and technology. And Solow, to his credit, admits that taxes on wealth would hurt economic growth and hurt workers. In a 2014 New Republic review of Piketty’s book , Solow wrote:

The labor share of national income is arithmetically the same thing as the real wage divided by the productivity of labor. Would you rather live in a society in which the real wage was rising rapidly but the labor share was falling (because productivity was increasing even faster), or one in which the real wage was stagnating, along with productivity, so the labor share was unchanging? The first is surely better on narrowly economic grounds: you eat your wage, not your share of national income.

Translation: If you want labor to get a bigger share of a smaller output, you might favor taxing wealth. But if you want labor to get more in absolute terms, you should oppose taxing wealth.

Nevertheless, Solow expresses sympathy for taxes on wealth. In the next two sentences of the paragraph quoted above, he explains why:

But there could be political and social advantages to the second option. If a small class of owners of wealth—and it is small—comes to collect a growing share of the national income, it is likely to dominate the society in other ways as well.

What are those advantages? He doesn’t say. That’s understandable in a book review, but even Piketty, in a 685-page book, doesn’t get around to saying how the wealthy would dominate society.

In a recent forum at the Peterson Institute for International Economics, Piketty’s sometimes co-author Emmanuel Saez of the University of California, Berkeley, made his case for a tax on wealth and claimed that the wealthy have disproportionate influence on economic policy. In a segment that is beautiful to see (from about the 1:07:00 point to the 1:09:30 in this forum), Larry Summers challenged Saez to give an example where reducing wealthy people’s wealth by 20 percent would produce better political, social, or cultural decisions. Summers to Saez: “You’ve been making this argument for years. Do you have one example?” Saez didn’t. Summers went on to make the point that very wealthy people can have large influence by spending a trivial percentage of their wealth. Even heavy taxes on wealth would leave them quite wealthy.

In his earlier presentation on the panel, Summers made another important point. He considered three activities that wealthy people engage in. Activity A is continuing to invest it productively. Activity B is consuming it—for example, by hiring a big jet and taking their friends to a nice resort. Activity C is donating it to causes and, if the causes are political, having even larger influence on political causes than they have now. Both B and C are ways to avoid a tax on wealth; A is not.

One final note. I know that politicians of all stripes lie, but one highly misleading line that Warren likes to use is that she’s asking the very wealthy to “pitch in two cents” line. I’ll put aside the fact that she really means two percent. She knows that and, hopefully, the vast majority of her audience knows that. My big problem is the word “asking.” She’s not asking; that’s not how the IRS operates. Warren is threatening to use force on those who don’t comply: that’s the “man with the gun over there” in the Buffalo Springfield song quoted at the start of this essay.

A tax aimed at the wealthy is a bad idea on philosophical and economic grounds. Let’s hope both Senators Sanders and Warren pay the price for their proposed assault on the wealthy and, indirectly, their assault on the rest of us.

More evidence that climate sensitivity to CO2 is way less than the popular climate models say

Frank Bosse at Judith Curry's blog.

FB's study suggests that the temperature rise associated with a doubling of CO2 is about 1/3 of that suggested by the climate models used to generate climate alarm.

Using the popular climate models to predict climate change, along with statistical uncertainty bands, strikes me as disingenuous.

The popular climate models do not incorporate important variables and only approximate reality for the variables they do include.  Given what they leave out, the approximation is poor.  They also treat climate sensitivity as a parameter used to fit the models to recent data, along with other parameters.  Thus, they constitute "curve fitting", a notoriously poor procedure for prediction outside the data range.  Also, curve fitting does not satisfy the statistical assumptions used to derive uncertainty bands for these predictions.  So, the popular climate models are not credible for forecasting climate change into the future.  Of course, the models' purveyors, the media, and politicians do not tell you about this.

Those talking about the "existential threat" of climate change are not credible.
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Equilibrium climate sensitivity computed from the latest energy imbalance data.

The Earth Energy Imbalance (EEI) is a key issue for estimating climate sensitivity.

If EEI is positive then the Earth’s climate system gains energy; if it’s negative the system loses energy, largely due to the energy flow into or out of the oceans.

A recent paper, Dewitte et al (2019), henceforth D19, derives changes in the EEI during the period 2000-2018, using data from the satellite CERES mission.

They shift the CERES values so that their average matches an EEI estimate from another study that is based on in-situ ocean heat content (OHC) data from ARGO buoys, and drift-correct them.

D19 concludes:

“At first sight it seems surprising that the EEI is decreasing during a period of continued greenhouse gas emission.”

Fig.1: The slightly decreasing EEI trend (green) during 2000…2018. (Source: Fig. 14 from D19)

It is indeed surprising that the EEI not climbed during the last 19 years when taking into account the ongoing increase of forcing, arising mainly from rising greenhouse gas levels.

In D19 the authors considered the plausibility of this outcome. They bolster the result with inspection of OHC data, calculating the time derivative dOHC/dt (which represent ~93% of the EEI) and the trend in it.

It’s not the only paper which estimates a near zero EEI trend in the 21st century. Also a review paper ( Meyssignac et al (2019)) comes to this outcome, see their Fig. 12 for 2006…2016. For a further check I calculated the derivative dOHC/dt for two year intervals, which are a measure of the EEI ( not the absolute OHC, see this report, section 2b) from three observational OHC products ( Domingues/Levitus; Ishii; Cheng) from this source.

The fourth cited dataset, Resplandy et al (2018), I skipped due to the retraction of the related paper, the mindful reader will remember.

The development of the EEI deduced from Cheng, this dataset was also used in L/C 18:



Fig.2: The dOHC/dt development with a 15 years Loess smooth

The result gives a very similar picture, indicating a near zero (or even a slightly falling) trend during 1999….2018 for the EEI.

What does this mean for the climate sensitivity?

Equilibrium/effective climate sensitivity (ECS) can be estimated as the (scaled) slope of the relationship between observed Global Mean Surface Temperature (GMST) and the excess of effective radiative forcing (ERF) over EEI, provided that the influence of natural climate system internal variability is small enough over the analysis period.

When there is an EEI standstill over a given period, then during this time the slope of the relationship between the observed GMST and the ERF reflects the climate sensitivity in equilibrium.

Sensitivity estimate for 1999…2018

The observed time span is very short for this purpose, only 20 years. This limits the toolbox available for doing calculations. In Lewis/Curry (2018) (LC18) the authors take changes between base and -final periods for both ERF and GMST data, see their section 4.

This avoids some pitfalls from the dilution problem of regression approaches which biases the slope estimations low. However, that method is only suitable with long enough time windows. Therefore I apply the regression method, including all annual data, in this case not using OLS (for Ordinary Least Square) regression but Deming regression. This method takes into account the uncertainties in variables from both datasets used, ERF and GMST, and should avoid the regression dilution problem.

The short time window will make optimizing the S/N ratio very crucial due to the fluctuating non-anthropogenic influences. Therefore I tried to reduce the “climate noise” in the GMST dataset- HadSST4 based Cowtan and Way (C&W) in this case.

I adjusted it for ENSO, solar and volcano influences, very similarly as was shown here. The “filter” was developed by Grant Foster aka “tamino”, released here.

The ERF data used are the same as used in L/C18, updated by the lead author to 2018.

Results

Fig.3: Deming Regression of the ERF on filtered GMST for 1999…2018 when the EEI was in a temporary standstill. All estimated natural forcing and ENSO variability was filtered out in the GMST, therefore the total anthropogenic ERF is used.

The trend slope reflects the observed climate feedback parameter λ (in W/m²/K).

The R² of the calculated trend is 0.88, which is a remarkably high value, when one takes the short time span involved into account.

The derived ECS best estimate (based on an ERF of 3.8 W/m² when doubling the CO2 content of the atmosphere) is:

3.8 W/m² / 2.21 W/m²/K =1.72 K

Conclusion

I calculated the climate sensitivity in a temporary standstill period (or slightly decreasing) as it was detected in the observations of the EEI during 1999 to 2018. The ECS value of 1.72K as the best estimate is in excellent agreement with the value found in LC18, 1.66K using the then current C&W GMST dataset (see Tab.3 of this paper).

The published ECS-values of the CMIP6 models have a mean above 4 K (see this recent paper) that is higher by a factor of 2.4 than observed here. This growing discrepancy between observed values of ECS reduces the credibility of the high model estimates.