Political Calculations
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September 30, 2016

Not long ago, Katie Burke suggested in American Scientist that the term pseudoscience should stop being used, arguing that:

Using the term pseudoscience... leads to unnecessary polarization, mistrust, disrespectfulness, and confusion around science issues. Everyone—especially scientists, journalists, and science communicators—would better serve science by avoiding it.

But would that really be true? And what descriptions should replace the term pseudoscience if that is true?

Burke offers some suggestions that gets into the specific context in which the alternatives might apply on either side of what has become known as the demarcation problem in describing science versus pseudoscience, which can be thought of as the challenge of distinguishing between credible scientific effort in new or less-studied fields and outright fraud that has been dressed up and presented in scientific clothing:

There are great alternatives to the term pseudoscience—ones that are much more explicit and constructive. One can simply state what kind of scientific evidence is available. If scientific evidence is lacking, why not say so and then discuss why that might be? And if scientific evidence directly contradicts a claim, saying so outright is much stronger than if a fuzzy term like pseudoscience is used. Of course, if fraudulent behavior is suspected, such allegations are best stated overtly rather than veiled under the word pseudoscience. Words that are clearer, stronger, and avoid such fraught cultural and historical baggage include the following: For the first use of the term, I suggest replacing pseudoscience with descriptors such as emerging and still-experimental, as yet scientifically inconclusive, scientifically debated, and lacking scientific evidence. For the second use of the term, stronger wording is appropriate, such as fraud (suspected or proven), fabrication, misinformation, factually baseless claims, and scientifically unfounded claims. All these terms make the nature of the information clearer than invoking the word pseudoscience and they have established journalistic norms around their substantiation.

Steven Novella responded to Katie Burke's arguments in Neurologica's blog, defending the use of the term pseudoscience as it has come to be commonly used in all its meanings. More importantly, he takes on the demarcation problem directly.

Burke refers to the demarcation problem, the difficulty in distinguishing science from pseudoscience, but derives the wrong lesson from this problem. The demarcation problem is a generic philosophical issue that refers to distinctions that do not have a bright line, but are just different ends of a continuum.

Arguing that the two ends are meaningless because there is no sharp demarcation is a logical fallacy known as the false continuum. Even though there is no clear dividing line between tall and short, Kareem Abdul Jabar is tall, and Herve Villechaize is short.

Rather than discarding a useful idea because of a demarcation problem, we simply treat the spectrum as the continuum that it is. What this means is that we try to understand the elements that push something toward the science or pseudoscience end of the spectrum.

Novella then goes on to identify the features of pseudoscience that make its illegitimacy recognizable as such on the pseudoscience end of the continuum.

  • Cherry picks favorable evidence, often by preferring low quality or circumstantial evidence over higher quality evidence
  • Starts with a desired conclusion and then works backward to fill in apparent evidence
  • Conclusions go way beyond the supporting evidence
  • Fails to consider plausibility, or lacks a plausible mechanism
  • Dismisses valid criticism as if it were personal or part of a conspiracy. This is part of a bigger problem of not engaging constructively with the relevant scientific community
  • Violates Occam’s Razor by preferring more elaborate explanations or ones that involve major new assumptions over far simpler or more established answers
  • Engages heavily in special pleading
  • Tries to prove rather than falsify their own hypotheses
  • Not self-correcting – does not drop arguments that are demonstrated to be wrong or invalid.

We can certainly attest to having directly witnessed each one of these unscientific or unprofessional behaviors since launching our "Examples of Junk Science" series.

Novella then puts his finger directly on the real distinction between science and pseudoscience:

Science vs pseudoscience are about process, not conclusions, and therefore not values and beliefs (except for valuing science itself – and that is the entire point)....

In the end this is not about us vs them (again, this has been exhaustively discussed already in the skeptical literature). It is about understanding the process of science and all the ways in which that process can go wrong or be deliberately perverted.

That is pseudoscience. It is worth understanding and it is helpful to label it honestly.

There's a lot of discussion that falls in the section covered by the dot-dot-dot of the ellipsis in the quoted passage above, where we cannot recommend reading the whole thing strongly enough!

Science vs Pseudoscience Demarcation Problem

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September 29, 2016

In the course of our projects, we occasionally come across data that is pretty interesting in and of its own accord. In the following chart, we've opted to show the cumulative distribution of individual income for the United States, China and the World, as expressed in terms of U.S. dollars (USD) adjusted for their purchasing power parity (PPP).

Cumulative Distribution of Individual Income, 2013

Since the data applies for 2013, we've also opted to indicate the U.S. poverty threshold for a single individual for that year, $11,490, and have estimated the income percentile into which someone with that income in the U.S. (21.6), China (94.0) or the World (86.6) would fall.

Additional food for thought: Extended Measures of Well-Being: Living Conditions in the United States: 2011, which gives an indication of the relative wealth of the impoverished portion of the U.S. population.

Data Sources

Hellebrandt, Tomas, Kirkegaard, Jacob Funk, Lardy, Nicholas R., Lawrence, Robert Z., Mauro, Paolo, Merler, Silvia, Miner, Sean, Schott, Jeffrey J. and Veron, Nicolas. Transformation: Lessons, Impact, and the Path Forward. Peterson Institute for International Economics Briefing. [PDF Document]. 9 September 2015.

U.S. Department of Health and Human Services. 2013 Poverty Guidelines. [Online Document]. Accessed 27 August 2015.

U.S. Census Bureau. Current Population Survey 2014 Annual Social and Economic (ASEC) Supplement. 2013 Person Income Tables. PINC-01. Selected Characteristics of People 15 Years Old and Over by Total Money Income in 2013. Total Work Experience, Both Sexes, All Races. [Excel Spreadsheet]. 16 September 2014.

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September 28, 2016

The Kaiser Family Foundation and the Health Research & Educational Trust have released the results of their 2016 Employer Health Benefits Survey, which gives an idea of how much the health insurance coverage provided by U.S. employers costs.

Those costs are divided between employers and their workers. In the case of health insurance premiums, the cost is shared between U.S. employers and workers. For 2016, U.S. employers will pick up an average of 82% ($5,309) of the full cost of the premiums ($6,438) for workers who select single coverage and an average of 71% ($12,865) of the full cost of health insurance ($18,142) for workers who select family coverage.

U.S. employees however are fully responsible for paying the deductible portion of their health insurance coverage, which is the actual cost of the health care they might actually consume before they would realize the full benefits of having health insurance coverage. For 2016, the average deductible for any type of health insurance is $1,478 for single coverage and we estimate an average deductible of $2,966 applies for family coverage.

Combined together, these costs represent the amount of money that the average American employee can expect to pay before their health insurer would begin paying the majority of costs for the actual health care they consume. The following chart indicates the average annual costs for employers and employees for health insurance premiums and deductibles in 2016.

2016 Average Costs of Health Insurance Premiums and Deductibles for Employed Americans

Most of these values are directly provided in the 2016 Employer Health Benefits Survey, however we've estimated the average cost of the deductibles for employees selecting family health insurance coverage by calculating the weighted average deductible that applies for each major category of health insurance coverage according to the percentage enrollment for each plan type in 2016, whether conventional, Health Maintenance Organization (HMO), Preferred Provider Organization (PPO), Point of Service (POS) or High Deductible Health Plan (HDHP).

For 2016, U.S. workers with single health insurance coverage will pay 33% of the combined total cost of health insurance premiums and deductibles before reaching the threshold where the health insurer is responsible for paying the majority of their health care expenses. U.S. workers with family health coverage can expect to pay up to 39% of the combined total cost of health insurance premiums and deductibles before they reach that threshold.

U.S. workers pay no income taxes on the amount of money their employers contribute to paying their health insurance premiums on their behalf. That exemption has existed since World War 2, when the U.S. government passed legislation to allow U.S. firms to provide these alternative compensation benefits in order to attract and retain skilled employees at a time when the wage and price controls of that era prevented them from being able to directly pay them more.

Data Source

Kaiser Family Foundation and Health Research & Educational Trust. 2016 Employer Health Benefits Survey. Exhibits 5.1, 6.3, 6.4, 7.7 and 7.20. 14 September 2016.

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September 27, 2016

In the private sector, the decision to take on debt can be considered to be a bet on the future.

By and large, when a new debt is incurred, it means that both borrower and lender have made a determination that the borrower will have sufficient income over the period defined by the terms of the loan to pay back the money they borrowed. To the extent that borrowed money will paid back with the income from newly generated economic activity, private sector debt can provide an indication of economic growth.

More specifically, the change in the acceleration (called "snap" or "impulse") of private sector debt can indicate the direction of the growth of the economy, where a positive impulse would be consistent with expansionary conditions being present in the economy and a negative impulse being consistent with the presence of contractionary forces in the economy.

Three months ago, we speculated that the U.S. economy would benefit from a positive change in the acceleration of private sector debt during the second quarter of 2016. And sure enough, that's what happened - and really for the first time in years, it has happened without the assistance of quantitative easing.

Acceleration (Change in Year Over Year Compounded Growth Rate) of Private Debt in the United States, January 2006 - June 2016

From 2016-Q1 to 2016-Q2, the real growth rate of the U.S. economy ticked upward from 0.8% to 1.1%, coinciding with a positive shift in the acceleration in the growth of private sector debt during the quarter. At the same time however, the trailing year average for the real GDP growth rate trended downward, largely as a result of the headwinds faced by the oil production and manufacturing sectors of the U.S. economy.

Annualized Real GDP Growth Rate in the United States, January 2006 - June 2016

Looking toward the current quarter, there is early evidence that points to an increase in the acceleration of private debt in the U.S. economy in the early months of 2016-Q3, suggesting a more positive outcome for GDP than was recorded in 2016-Q2 lies ahead.

References

U.S. Federal Reserve. Data Download Program. Z.1 Statistical Release (Total Liabilities for All Sectors, Rest of the World, State and Local Governments Excluding Employee Retirement Funds, Federal Government). 1951Q4 - 2016Q2. [Online Database]. 16 September 2016. Accessed 16 September 2016.

National Bureau of Economic Research. U.S. Business Cycle Expansions and Contractions. [PDF Document]. Accessed 16 September 2016.

U.S. Bureau of Economic Analysis. Table 1.1.1. Percent Change from Preceding Period in Real Gross Domestic Product.
1947Q1 through 2016Q2 (second estimate). [Online Database] Accessed 16 September 2016.

ClearOnMoney. Credit Impulse Background. [Online article]. Accessed 28 October 2015.

Keen, Steve. Deleveraging, Deceleration and the Double Dip. Steve Keen's Debtwatch. [Online article]. 10 October 2010. Accessed 28 October 2015.

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September 26, 2016

The third full week of September 2016 saw the month's second Lévy flight event, which began in the aftermath of the Federal Reserve's FOMC meeting on Wednesday, 21 September 2016. We can see the result of that event, which coincides with investors appearing to have shifted their forward-looking focus from 2017-Q1 back out to the more distant future quarter of 2017-Q2 in the following chart.

Alternative Futures - S&P 500 - 2016Q3 - Modified Model 01 - Snapshot on 2016-09-24

That shift occurred after the Fed indicated that it would once again hold off on increasing short term interest rates in the U.S., where investors had pulled in their forward-looking attention to the nearer term a little over two weeks earlier, as several Fed officials suggested they would support hiking those rates at the Fed's September meeting.

We don't think that investors were focused so much on 2017-Q1 as much as they were roughly evenly splitting their focus between the very near term quarter of 2016-Q3 and the distant future quarter of 2017-Q2. With the Fed's confirmation that they would not be hiking U.S. short term interest rates in 2016-Q3, investors quickly refocused their attention on the more distant future of 2017-Q2, with stock prices changing to be consistent with the expectations associated with that future quarter.

On a separate note, this upcoming week will see the end of our use of the modified futures-based model we developed back in Week 3 of July 2016 to address the effect of the echo of historic stock price volatility upon our standard futures-based model of how stock prices work, which we knew in advance would affect the accuracy of the model's forecasts during 2016-Q3.

We have enough data now to confirm that our modified model was much more accurate than our standard model during the past several weeks in projecting the future trajectories that the S&P 500 would be likely to follow given how far forward in time investors were collectively looking in making their current day investment decisions. The following chart shows how the echo of the extreme volatility that was observed a year earlier affected our standard model's projections.

Alternative Futures - S&P 500 - 2016Q3 - Standard Model - Snapshot on 2016-09-24

Although we show the echo effect from the previous year's volatility ending after 28 September 2016 in these charts, that may be somewhat misleading, as we're really entering a period which is reflecting the echo of a relatively short interlude in the greater stock price volatility that defined the latter half of 2015. As such, we expect that the trajectory of the S&P 500 will deviate from our standard model's projections during the next two weeks, but not as greatly as it did during the last several weeks.

We will have another prolonged opportunity to test drive our modified model in 2016-Q4.

Meanwhile, here are the headlines that caught our attention as newsworthy from their market-moving potential during Week 3 of September 2016.

Monday, 19 September 2016
Tuesday, 20 September 2016
Wednesday, 21 September 2016
Thursday, 22 September 2016
Friday, 23 September 2016

Elsewhere, Barry Ritholtz rounded up the week's positives and negatives for the U.S. economy and markets.

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