Yotta Point

Election 2014: Why Did Democrats Lose the US Senate?

I decided to return briefly from hibernation to do some analysis on this. My findings are posted in this presentation:

Election 2014: Why Did Democrats Lose The US Senate?

 

P.S. The PDF version is here.

Some Readings on Income and Wealth Inequality and Intergenerational Mobility

I have been trying to develop a deeper understanding on the topic of income and wealth inequality and wanted to bookmark for myself some pertinent research and posts.

Josh Barro [Business Insider]

• 95% Of Income Gains Since 2009 Went To The Top 1% — Here's What That Really Means
• David Brooks is Wrong About Inequality
• Sorry, Libertarians — Inequality Does Matter

Andrew Berg, Jonathan Ostry [IMF]

• Equality and Efficiency
• [with Tsangarides] Redistribution, Inequality, and Growth
• Inequality and Unsustainable Growth: Two Sides of the Same Coin?

Matthew O'Brien [The Atlantic]

• Why The 1% Don't Feel Rich

Miles Corak [University of Ottawa]

• Income Inequality, Equality of Opportunity, and Intergenerational Mobility; PDF
• How to Slide Down the ‘Great Gatsby Curve’: Inequality, Life Chances, and Public Policy in the United States

Kathleen Geier [Inequality Matters]

• Ten facts you should know about global economic inequality

Colin Gordon [Dissent]

• The Computer Did It? Technology and Inequality
• Who Pays? Taxes and American Inequality

David Howell [The New School]

Disaster Costs, GDP and Climate Change

Roger Pielke Jr. recently published an article in Nate Silver's new 538 website titled Disasters Cost More Than Ever But Not Because of Climate Change. You should click the link and read his entire piece but, his thesis is essentially the following:

1. The cost of disasters tied to extreme events is indeed rising rapidly - but once you normalize this to the increase in the wealth of the world as represented by global GDP (we own a lot more "stuff" now than before) - the trend basically vanishes
2. Disasters lead to high loss of life where property damage is the lowest and highest property damage in places with lowest loss of life. As examples he cites the following: "U.S. hurricanes, for example, are responsible for 58 percent of the increase in the property losses in the Munich Re global dataset" and "Consider that since 1940 in the United States 3,322 people have died in 118 hurricanes that made landfall. Last year in a poor region of the Philippines, a single storm, Typhoon Hayain, killed twice as many people."
3. Richer countries are in a better position to protect people's lives in times of disasters at the expense of higher property losses. He says: "As countries become richer, they are better able to deal with disasters — meaning more people are protected and fewer lose their lives. Increased property losses, it turns out, are a price worth paying"

His article was roundly criticized by some well known climate scientists, as summarized by Emily Atkin at Think Progress (additional discussion in this post by Laurence Lewis at Daily Kos). The crux of the criticism as I see it:

1. Improvements to deal with disasters (e.g., newer building codes and improved construction, better forecasting, better preparedness and responses) over the years reduces disaster damages/costs (lives and property) and this is not properly reflected in Pielke's assertions - this has been pointed out to him before, yet he continues to publish his misleading claim
2. Unlike Pielke's claims that somehow climate change is not causing an increasing frequency of extreme events, there are several situations where this is in fact the case

Pielke responded to these critiques in an email to Think Progress (same link as above):

Unionization and Jobs/Wages in Today's Economy

Evan Soltas’ blog posts and chart, following his Bloomberg View article ("Is the U.S. Better Off Without Unions?"), are quite interesting and worth reading. I tend to agree with his observations on why unionization has declined in the US (and apparently other developed countries) in the second half of the 20th century and I also question the focus on unions as a key method of addressing unemployment and wage pressure challenges faced by US workers. However, I am not convinced that Soltas' ideas to address the issue are that effective. So, I'd like to offer a different perspective on this subject given my background as an executive in the Silicon Valley tech industry. This perspective focuses on the after-effects of stock-based compensation on various macro factors.

First, some context. In Silicon Valley, tech labor has tended to be non-unionized for quite some time. Additionally, over the last two decades, I've seen software design, hardware manufacturing, hardware design and other functions move outside of the US (I have also been in positions where I have moved certain jobs overseas). The reasons are simple. There are qualified workers who can do these jobs outside the US, typically at a much lower cost. Moreover, when a company's competitors move jobs or are located overseas in cheaper labor markets, for financially competitive reasons the company ends up having to do the same. Additionally, some of the larger tech companies that sell directly in non-US end markets need workers in those regions. Despite this trend, and despite many tech jobs being non-unionized, Silicon Valley has managed to maintain a decent jobs picture (example) for well-paid employees - I think primarily because US tech companies continue to be at the forefront of driving significant innovation where more experienced US workers have an advantage over workers is job markets outside the US. My experience suggests that at least in Tech, where workers tend to be paid relatively well because of their skills, unionization has not been a significant factor in driving employment or wage trends. I can’t extrapolate from this to infer that the tech experience should be applied to every other industry but it is suggestive that we need to look for deeper explanations for increases in income inequality and decline in unionization in the US and other economies.

Deutsche Bank's Jobless Claims Indicator is Not Very Helpful in Predicting Recessions

Sam Ro at Business Insider points out:

According to most experts, the U.S. is still growing. However, some experts like [ECRI's] Lakshman Achuthan and [SocGen's] Albert Edwards argue that the U.S. is in a recession.

[...]

But Riccadonna and the economics team at Deutsche Bank have an incredibly reliable indicator of recessions.

They call it the "rule of 10%" and it's an interpretation of the U.S. weekly initial jobless claims. Riccadonna explains:

Our rule of thumb is as follows: In the past, when jobless claims backed up by 10% or more from the prior quarter’s average, in all but one instance (Q1 1967), the economy was on the brink of recession. Thus, the rule has proven to be fairly reliable over the past several decades.

Here's his chart showing the record of this indicator:

Using the above chart, Sam Ro concludes:

So based on the recent quarter's average jobless claims and the current trend of jobless claims, Riccadonna notes that that "rule of 10%" would suggest we are not heading into recession.

Riccadonna's and Ro's conclusion is unwarranted.

As I explained in a series of tweets, I don't know if we are currently in a recession or not, but Riccadonna's chart is not really useful to assess that at all. The reason is simple. Riccadonna and Ro already acknowledge that the above indicator incorrectly predicted a recession in 1967. However, they didn't notice that the chart failed to predict the start of the 2008 recession using their 10% threshold. If you look closely at the above chart, you will notice that at the time the 2008 recession began (or when the economy was "on the brink of recession"), the indicator was up ~5% q/q (a dip from a slightly higher number in the prior quarter), much less than the 10% q/q threshold cited by Riccadonna. It only rose to ~10% some months into the recession. So, the use of the 10% threshold by Riccadonna is incorrect and artificial. The correct threshold would be closer to ~5% - and if we use that threshold, the chart is revealed as basically not that useful because it would have predicted a recession in many more years that were not recession years. Not to mention the indicator is close to the ~5% q/q threshold right now.

Reads for June 24

It's been a while since my last round-up of interesting posts, so here is a reading list for this weekend (updated).

Economics & Finance

• Henry Blodget at Business Insider: Corporate Profits Just Hit An All-Time High, Wages Just Hit An All-Time Low
• Ben Cassel at Real Time Economics (WSJ): "Number of the Week: Corporations Not Hoarding Cash"
• Eric Platt at Business Insider: America's Debt Levels Are Falling To Pre-Crisis Levels - but also see David Cay Johnston at Reuters looking at income as well: "America's Long Slope Down" and Mish Shedlock's comments at Global Economic Analysis
• Tyler Durden at Zero Hedge: The Chart That Scares The "1%" The Most
• Joe Weisenthal at Business Insider: Yesterday's Philly Fed Report Implies A Horrendous Jobs Report
• Joe Weisenthal at Business Insider: The Difference Between Obama's First Term And Bush's First Term In One Devastating Chart
• New Deal Democrat at Business Insider: Inflation Is Under Control, And It's A Sign Of Bad Things
• Zarathustra at Also Sprach Analyst: Quantitative easing and the (lack of) responses in bond yields
• Paul Krugman at NYT: Interest Rates: Varieties of Error
• Scott Barber at Alpha Now/Thomson-Reuters: "Crude Oil Futures Bouncing Near Their Lows of 2012" - has interesting charts comparing historical and recent behavior of oil vs. economic indicators and stocks; also see Sober Look's related analysis: "Crude oil vs. US equities: dislocation continues"
• Sober Look: "Dr. Copper Says Watch Out Below" & "Summer of 2011 Redux"
• Mark Thomas at Economist's View: Did Income Inequality Contribute to the Great Recession?
• Jie Jenny Zou at Real Time Economics (WSJ): Inequality Has Increased in Income and Consumption, Economists Argue [via]; here's the link to the paper by Attanasio, Hurst and Pistaferri: "The Evolution of Income, Consumption, and Leisure Inequality in The US, 1980-2010" [and a related post on income inequality across recent Presidents here; links here and here worth a look as well]
• David Cay Johnston at Reuters: "The Fortunate 400"
• Econompicdata: "Valuation Matters: Equities vs Bonds Edition"; "Dividend vs. Treasury Yields"; "Stocks For The Long Run?"
• Jared Bernstein at On The Economy: "CBO’s Long-Term Budget Outlook: What It’s Telling Us"
• Zarathustra at Also Sprach Analyst: "Coal inventory in various Chinese ports hit record highs" [also see Sober Look's post on this]
• Zarathustra at Also Sprach Analyst: "Is China massaging macro data? (Ans: Yes)"
• Sober Look: "India's Currency Spirals Out of Control" [see this older post as well]

Energy & Environment, Climate Change

• Climate Central: The Heat is On: U.S. Temperature Trends
• National Research Council: Sea-Level Rise for the Coasts of California, Oregon, and Washington: Past, Present, and Future (2012)
• Climate Central has a neat interactive online tool that allows you to assess the impact of sea-level rise on your coastal town or city
• Stefan at Real Climate: "What makes sea-level rise?"
• Andrew Freedman at Climate Central: "Did Global Warming Set Stage for Duluth Flooding?"

Healthcare

• Lane Kenworthy at Consider the Evidence: "Is rising obesity a product of income inequality and economic insecurity?"; "Why the Surge in Obesity?"
• Duff Wilson and Janet Roberts at Reuters: "Special Report: How Washington went soft on childhood obesity"

Miscellaneous

• Elizabeth Flock at US News/Washington Whispers: "Five Charts That Show How Our Democracy Isn't Working"

That's it for today.

"Why Smart People Are Stupid" and Other Revelations About the Brain

UPDATE 6/24: I cited Jonah Lehrer below. This has to be read to be believed - Lehrer might as well have titled his article about himself.

One of the best books that I read recently is Nobel Prize winner Daniel Kahneman's "Thinking, Fast and Slow". You'll find a lot of reviews of the book online (e.g., Jim Holt at the NYT), but the best thing to do would be to pick up a copy of the book and read it (Sci Am has reprinted a portion recently). As one of the reviewers in Amazon.com, Adam Smythe, wrote [all bold text in this post is my emphasis]:

Daniel Kahneman, the author of this exceptional book, and Amos Tversky (who died in 1996) made economics and other disciplines a lot more realistic--and tougher--for economists, researchers and students. Prior to their work, economists and others maintained classical theories and explanations that relied on certain seemingly logical assumptions about human behavior. However, people don't always behave the way logic might suggest, for a variety of reasons that Kahneman (and Tversky) explained, starting in the 1970s. Today, the subject of behavioral decision-making is one of the more exciting ones in fields like economics, finance, medicine and even law, thanks to their pioneering work. In recognition of the impact of his work in economics, Kahneman, a cognitive psychologist and professor emeritus at Princeton, won the Nobel Prize in Economics in 2002, specifically for his work on prospect theory.

The title of this book comes from Kahneman's discussion of two simple models of how people think. "System 1" thinking corresponds to fast, intuitive, emotional and almost automatic decisions, though it sometimes leaves us at the mercy of our human biases. "System 2" thinking is more slow-going and requires more intellectual effort. To nobody's surprise, we humans are more likely to rely on System 1 thinking, because it saves us effort, even if it can lead to flawed thinking.

Andrew Revkin at Dot Earth (NYT) posted the video a recent talk by Kahneman and added:

As I noted via Twitter during the meeting, this talk and many other engaging presentations at the event illustrate the importance of adding a fresh facet to the popular notion that today’s citizens, and particularly students, would do well to improve their capacity for critical thinking:

“Critical thinking has to include assessing one’s own thinking.”

Last week, Jonah Lehrer at The New Yorker, mentioned Kahneman's work and wrote about another recent study whose implications are important in a post titled "Why Smart People are Stupid". Here are some relevant extracts from Lehrer's piece, but go read his entire post:

Stocks and Bonds Diverge

UPDATED 2/22/12

A few people have been discussing this topic of late and it's worth a mention in the context of what to expect vis-a-vis US economic growth in the coming months.

The observation is that as market confidence in a US economic recovery has become stronger, stock markets have shot upwards but bond yields have not. One would normally expect bond yields to also go up as money moves from bonds to stocks during economic recoveries. This "anomaly" is interesting and worth watching.

Tyler Durden at Zero Hedge (1/30/12): "Yields Plunge Most In 3 Months As Equity-Debt Divergence Remains"

Joe Weisenthal at Business Insider (2/7/12): "PRESENTING: The Great Market Disconnect Seen All Around The Globe"

Joe Weisenthal at Business Insider (2/18/12): "This Is STILL The Most Glaring Anomaly In The Market"

Tyler Durden at Zero Hedge (2/20/12): "As Everything Disconnects And Everything Is Soaring, Morgan Stanley Issues A Warning"

[Also worth a look - this Aug 2011 post  by Felix Salmon at Reuters: "Chart of the day: The great earnings-yield divergence"]

It's also noteworthy that stock market trading volumes have been noticeably lower in recent times - and it's happening at a time where revenue and earnings growth has also declined (outside of Apple) and upside guidance from companies is also on the decline.

P.S. Joe Weisenthal has an observation from Doug Kass that there is also a marked divergence between the S&P 500 vs. the ratio of Dow Transports to the S&P 500. Transports, I would presume are at least in part affected by rising oil prices at a time when overall world oil consumption has been on the decline. Additionally, the potential impact of QE and liquidity type actions on stock markets and commodities like oil can't be emphasized enough.

Do Surveys of Consumer Confidence Provide Insight Into Actual Economic Activity?

Sober Look has two good, worth reading posts - "The story of two risk indices" and "The jump in U Michigan sentiment index is driven by consumer expectations". I'll try to return to the first post later but I thought I'd comment on the second one - which is about indicators tied to what we generally refer to as 'consumer sentiment' or 'consumer confidence'. SL makes some good points about these indices but he does not mention a larger point that I want to call out here - which is that these indices very clearly are not at all good gauges of absolute economic activity.

1) Let's start with SL's first point that the Thomson Reuters/University of Michigan's survey of consumer sentiment registered a reading in August 2011 that was as low as the reading in November 2008 - which, as SL says is "...a bit surprising and not at all intuitive, with anecdotal evidence suggesting that 08 felt far worse than 2011".

He's completely right about the non-intuitive nature of index values - but it is also completely non-intuitive when you look at non-anecdotal evidence. For example:

• The change in real GDP in Q4 2008 was -8.9% whereas in Q3 2011, GDP changed by +1.8%
• Retail sales changed at an annual rate of  more than -10% in Q4 2008 compared to +6 to 8% in Q3 2011
• Industrial production and capacity utilization were much lower in Q4 2008 compared to Q3 2011
• The economy was losing jobs at the average rate of ~600,000 jobs/month in Q4 2008 as opposed to gaining on average ~150,000 jobs a month in Q3 2011

So, what exactly is the Thomson Reuters/University of Michigan Consumer Sentiment measuring?

2) Second, SL adds:

What drove the sentiment index to levels comparable to 2008? The index actually has two components: the Current Conditions Index and the Consumer Expectations Index. And it was the "expectations" component that pushed the whole index to this low point. In fact according to U Michigan, when it came to consumer expectations, the "end of the world" was coming in August of 2011 - a point on the index that was in fact lower than the expectations measure in 2008 (as shown in the chart below). This is puzzling. Were consumers reacting to all the market driven media frenzy in August that wasn't as prevalent in 2008? Were consumers not as focused on the state of the financial markets in the past as they are now?

On the other hand the "current conditions" component did not dive anywhere close to its 08 lows, which is more in line with other economic indicators.

So, basically, according to this measure of consumer sentiment, in August 2011 the Consumer Expectations index fell lower than the lowest value in 2008! Yet, all the above indicators measuring the real health of the economy (see above) were much worse in Q4 2008 than in Q3 2011 and Q4 2011. How is it that, not just the overall consumer sentiment index, but also its consumer expectations component, seems so out of whack with macroeconomic conditions (current and immediate future)?

3) Third, SL discusses another indicator on consumer confidence:

Average Scaling Behavior in Biology, Cities and Corporations

I recently started reading some research from Geoffrey West, Luis Bettencourt and others on scaling phenomena relating to averages (there are always deviations). This is an incredibly rich topic and one cannot possibly do justice to it in a single post, so I'll merely highlight one or two findings from the research and provide links for further reading.

A good place to start is this article on biological organisms in Physics Today from 2004, by Geoffrey West and James Brown - "Life's Universal Scaling Laws". A couple of key passages (all bold text corresponds to my emphasis):

In marked contrast to the amazing diversity and complexity of living organisms is the remarkable simplicity of the scaling behavior of key biological processes over a broad spectrum of phenomena and an immense range of energy and mass. Scaling as a manifestation of underlying dynamics and geometry is familiar throughout physics. It has been instrumental in helping scientists gain deeper insights into problems ranging across the entire spectrum of science and technology, because scaling laws typically reflect underlying generic features and physical principles that are independent of detailed dynamics or specific characteristics of particular models. Phase transitions, chaos, the unification of the fundamental forces of nature, and the discovery of quarks are a few of the more significant examples in which scaling has illuminated important universal principles or structure.

In biology, the observed scaling is typically a simple power law: Y = Y₀M^b, where Y is some observable, Y₀ a constant, and M the mass of the organism.^1-3 Perhaps of even greater significance, the exponent b almost invariably approximates a simple multiple of 1/4. Among the many fundamental variables that obey such scaling laws - termed "allometric" by Julian Huxley⁴ - are metabolic rate, life span, growth rate, heart rate, DNA nucleotide substitution rate, lengths of aortas and genomes, tree height, mass of cerebral grey matter, density of mitochondria, and concentration of RNA.

[...]

An intriguing consequence of these "quarter-power" scaling laws is the emergence of invariant quantities,⁷ which physicists recognize as usually reflecting fundamental underlying constraints. For example, mammalian life span increases as approximately M^1/4,  whereas heart rate decreases as M^-1/4, so the number of heartbeats per lifetime is approximately invariant (about 1.5 x 10⁹), independent of size...

Bettencourt and West discussed cities and their economics and laws of scaling in their article in Scientific American last year titled "Bigger Cities Do More With Less". Here are some notable points:

By sifting through this flood of data, covering thousands of cities around the world, we have unveiled several mathematical “laws” that explain how concentrating people in one place affects economic activity, return on infrastructure investment and social vitality. Despite the rich diversity of metropolitan regions across the U.S., China, Brazil and other nations, we found a remarkable universality in the way that socioeconomic characteristics increase with a city’s population. For example, if the population of a city is doubled, whether from 40,000 to 80,000 or from four million to eight million, we systematically see an average increase of around 15 percent in measures such as wages and patents produced per capita. If eight million people all live in one city, their economic output will typically be about 15 percent greater than if the same eight million people lived in two cities of half the size. We call this effect “superlinear scaling”: the socioeconomic properties of cities increase faster than a direct (or linear) relation to their population would predict.

The data also reveal that cities’ use of resources follows a similar, though inverted, law. When the size of a city doubles, its material infrastructure—anything from the number of gas stations to the total length of its pipes, roads or electrical wires—does not. Instead these quantities rise more slowly than population size: a city of eight million typically needs 15 percent less of the same infrastructure than do two cities of four million each. This pattern is referred to as sublinear scaling. On average, the bigger the city, the more efficient its use of infrastructure, leading to important savings in materials, energy and emissions.

Our findings also show that these patterns of increased productivity and decreased costs hold true across nations with very different levels of development, technology and wealth. Although we have much more information for cities in richer parts of the world, we are beginning to obtain good data from rapidly developing countries as well, and they seem to fit the same mold. The gross domestic product for cities in Brazil and China, for instance, closely follows the same superlinear curve that western European and North American cities exhibit, though starting from a lower baseline. We believe that the pattern holds true because the same basic social and economic processes are at work, whether in São Paulo’s favelas, under Beijing’s smog-filled skies or along Copenhagen’s tidy streets.

Although urban superlinear scaling, which represents the average, idealized behavior of a city of a given size, prevails around the globe, actual cities deviate to varying degrees from the roughly 15 percent enhancements that come with size. Detailed data covering 40 years show, for example, that San Francisco and Boston are richer than their size would indicate, whereas Phoenix or Riverside, Calif., are somewhat poorer. Curiously, these deviations persist for decades: cities tend to stay remarkably close to their overperforming or underperforming histories. For example, cities that have attempted to improve their lot by creating conditions for the “next Silicon Valley” have often had disappointing results. Our research suggests that certain intangible qualities of social dynamics—more than the development of material infrastructure—hold the key to generating virtuous cycles of innovation and creation of wealth. These processes, such as the development of a spirit of local entrepreneurship, a reputation for cutting-edge novelty, and a culture of excellence and competitiveness, are difficult to design through policy because they rely on the dynamics of a city’s social fabric across many dimensions. We expect the results of this exciting area of research will lead to better “recipes” for sustainable socioeconomic development.

See the Physics Today article as well this article for some discussion of West's work, including some of the criticisms of the averages and how they miss various deviations from the averages. A recent paper that critiques the Bettencourt/West research on cities is this one by Cosma Shalizi, who says: