Political Calculations
Unexpectedly Intriguing!
March 29, 2017

A lot of business and economic data shows seasonal patterns. One example of data that shows a remarkably seasonal pattern is the total value of shipments delivered by beverage manufacturers to their distributor customers, which often precedes the actual consumption of these bottled and canned drinks by their end consumers in anywhere from a matter of days to several weeks.

The following chart shows that data for U.S. beverage manufacturers for each month from January 1992 through January 2017.

Total Value of Beverage Manufacturers' Shipments, Not Seasonally Adjusted,  January 1992-January 2017

In this chart, we can see that the not seasonally-adjusted data follows a remarkable similar pattern from year to year, which has strongly persisted over time even as the rolling 12-month average of these shipments has more than doubled from 1992 through 2016. Perhaps more remarkably, we can see the overall magnitude in the swings from one season to the next grow in size as the total value of shipments has increased.

To better visualize that seasonal variation, we calculated the mean value of beverages shipped each month by their U.S. manufacturers in each year, then calculated the percentage that each month's total shipment value represents with respect to that average value, where a value of 100% would coincide with a month's shipments being equal to the annual average for the year in which it occurred. The results of that math are shown in the following chart.

Value of Beverage Manufacturers' Shipments by Month as Average of Annual Average Value of Shipments, 1992-2016

With the data indexed with respect to each year's average total shipment value, we see that the seasonal swings are really pretty consistent over time, where the increasing magnitude indicated by the first chart is a result of the same size percentage swings being applied to the increased overall value of shipments.

At the same time, we see that January in each year marks the lowest number of shipments, coming in at roughly 85% of the average monthly value recorded during the year. The value of shipments then predictably increases through March, holds flat in April, then resumes increasing before typically peaking in June. Much of this pattern coincides with rising temperatures in the U.S. as the weather transitions from winter to spring and then to summer, which represents the peak period for manufactured beverage consumption in the U.S.

In July, the total value of beverage shipments falls in each year before rising to peak once more in August, which is then followed by a relatively steady decline in each following month through December. The cycle then repeats with the value of beverage shipments crashing to their January lows in the next year.

The June-July-August peak-dip-peak is also remarkable in showing the advance delivery of beverages ahead of their periods of peak consumption, where June shipments supply consumption during the U.S.' Fourth of July holiday and the August shipments are supporting consumption during the period of the U.S. Labor Day holiday, which falls on the first Monday in September each year.

This data is something that we dug up as part of another project, which we will get to in the very near future. For now, we thought it was perhaps interesting enough to present on its own as a data visualization exercise, and because major U.S.-owned beverage manufacturers like Coca Cola (NYSE: KO), PepsiCo (NYSE: PEP), Dr. Pepper Snapple Group (NYSE: DPS), National Beverage Corporation (NASDAQ: FIZZ) and a considerable number of smaller public firms, private firms and foreign-owned beverage manufacturers with U.S. production facilities could use a hug!

Data Source

U.S. Census Bureau. Beverage Manufacturing: U.S. Total, Not Seasonally Adjusted Value of Shipments [Millions of Dollars], Period: 1992 to 2017. Manufacturers' Shipments, Inventories, and Orders. [Online Database]. Data Extracted on: 28 March 2017.

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March 28, 2017

We're tied up with other projects today, but for some quick fun, we threw together the following chart showing the trajectory of the Dow Jones Industrial Average (DJIA) since 8 November 2016 (a.k.a. "The Age of Trump"), where we've noted some unusual winning and losing streaks in just the past two months....

Longest Winning and Losing Streaks in the Dow Jones Industrial Average From 8 November 2016 Through 27 March 2017

Going back to 2 May 1885, there have been a total of 4 winning streaks where the DJIA closed higher than its previous day's close over 12 consecutive trading days, with the fourth just having ended on 27 February 2017.

By contrast, there have been 43 losing streaks where the DJIA closed lower than it's previous day's closing value on 43 separate occasions since 2 May 1885, where the current losing streak through yesterday, 27 March 2017 may not yet be over.

Should the current losing streak extend a ninth day, it will mark just the twelfth time in the last 132 years where the Dow has dipped for that many days in a row.

Data Source

Williamson, Samuel H. "Daily Closing Values of the DJA in the United States, 1885 to Present," MeasuringWorth, 2017. URL: http://www.measuringworth.com/DJA/. Accessed 28 March 2017.

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March 27, 2017

We've had a remarkably good run in forecasting where the S&P 500 was likely to go over the past several months, but that almost perfect track record came to an end on Tuesday, 21 March 2017 when investors suddenly shifted their forward-looking attention away from 2017-Q2 to focus more strongly upon the more distant future of 2017-Q3.

Alternative Futures - S&P 500 - 2017Q1 - Standard Model - Snapshot on 24 March 2017

While we had anticipated that the volatility of the S&P 500 would be very likely to increase, the timing of the "screeching halt" to which the S&P 500's extraordinarily low level of volatility over the last several months came as its "history-setting trading streak" ended on 21 March 2017 caught us by surprise. You can see that surprise in the chart above where we had shown our short-term forecast range (the red-lined box) extending through 22 March 2017.

However, the dynamics that coincided with that sudden shift were those that we described in our notes last week. At present, we think that investors are still splitting their attention between 2017-Q2 and 2017-Q3 in setting current-day stock prices, but are currently placing a higher weighting on 2017-Q3 in doing so, which given where stock prices were, meant that stock prices would fall.

Right now, given the level of stock prices between the two alternative trajectories that the S&P 500 would be following if investors were exclusively focused on either 2017-Q2 or 2017-Q3 in setting their future expectations, we think that investors are now placing a 62% probability that the Fed will be compelled by poor economic data to delay its next intended increase short term U.S. interest rates into that more distant quarter.

The significant difference in the future expectations for 2017-Q2 and for 2017-Q3 is what will lead the S&P 500 to experience higher-than-recent levels of volatility during the next several months, where good news will lead investors to focus in the nearer term (as the Fed will be more likely to next hike interest rates sooner), and where bad news will lead investors to focus on the more distant future, where the Fed will delay its next rate hike.

Next week, we'll show you what that potential trading range will look like over 2017-Q2. Until then, here are the headlines that we identified as noteworthy over Week 4 of March 2017.

Monday, 20 March 2017
Tuesday, 21 March 2017
Wednesday, 22 March 2017
Thursday, 23 March 2017
Friday, 24 March 2017

For a bigger picture of the week's more notable news, Barry Ritholtz lists the week's positives and negatives for the U.S. economy and markets. (Spoiler alert: the week had more negatives than positives!)

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March 24, 2017

Not long ago, Core77's Rain Noe ordered a battery from Staples that was delivered to his home in a box that was way bigger than the battery enclosed inside.

It turns out that delivery was the unintended consequence of a decision by Staples, one that actually saves the company quite a lot of money, to both standardize the size of the corrugated (cardboard) packaging in which in ships goods to its online customers and to automate as much of the packaging operations at its fulfillment center as it can. A Core77 reader found a two minute-long video that describes how Staples fulfillment center packages the goods it ships.

So how did this choice by Staples lead to such a seemingly wasteful mismatch between the size of ordered good and the size of the packaging in which it was delivered? Rain explains:

... it appears that Staples has chosen the sizes of corrugated Z-fold most common to their order, with my tiny battery being an anomaly.

But that's not the end of the story. Packsize, the maker of the automated packaging equipment that Staples uses, recognizes the opportunity it has to benefit in the market from continuing to minimize the waste that results from shipping products to customers in oversize packages by better tailoring its on-demand packaging product line to produce "right-sized" boxes.

And that's the future of packaging. As for Packsize, the company often contracts with its customers to provide them its packaging machines at no cost, where its revenue comes from selling the Z-fold corrugated cardboard packing material used by the machines to the companies that acquire them. Or as Rain notes:

It looks like the razor-and-handle business model works well here.

Speaking of which, if you weren't already familiar with the BBC's 50 Things That Made the Modern Economy series of podcasts, here are links to a few of its episodes that directly overlap with the modern business of packing and shipping:

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March 23, 2017

Beginning in 2014, millions of lower income-earning Americans became eligible to have fully government-subsidized health insurance coverage through the U.S. government's Medicaid welfare program thanks to the expansion of eligibility for that program provided for by the Affordable Care Act (ACA), which is more popularly known as Obamacare. Unfortunately, that expanded access to health care may very well have caused an increase in death rates due to drug overdoses in the United States to such a degree that the overall estimated life expectancy of Americans has declined.

The influence of Obamacare's expansion of health care provided through Medicaid can be seen by comparing the death rates due to drug overdoses in the 28 states (and the District of Columbia) that chose to expand the enrollment of their state's Medicaid programs with the death rates in the 22 states that chose to not expand their state's Medicaid enrollment as part of the Affordable Care Act in the years before and after its implementation. In the following chart, we've indicated the highest, lowest, average (mean) and median death rates recorded among the individual states that participated in the Medicaid expansion.

Age-Adjusted Death Rates per 100,000 Population Due to Drug Overdoses in 28 Medicare Expansion States and District of Columbia, 2010-2015

Starting with the lowest death rates per 100,000 population reported among the Medicaid-expansion states, which mostly applies for North Dakota (for which no reliable data was available for 2011, where Iowa's data marks the low end of the scale in that year), we see that the pre-Medicaid expansion trend was essentially flat from 2010 through 2013, followed by a sharply rising trend in 2014 through 2015.

At the high end of the scale, where the data applies for the state of Washington), we see an overall rising trend from 2010 through 2013 (with a spike in 2011, which may be highly relevant in this discussion because Washington was one of six states to implement the early expansion of its Medicaid program in that year), followed by a much sharper increase from 2014 through 2015.

That overall pattern of slowly rising trend in 2010-2013 and much more sharply increasing rate of deaths from drug overdoses after Obamacare's wider expansion of Medicaid enrollments in these states from 2014 through 2015 is also evident in the mean (average) and median death rates recorded among the individual states in this grouping.

But what about the states that didn't expand their Medicaid enrollments as part of the Affordable Care Act? The following chart looks at the similar highest, lowest, mean and median data for death rates per 100,000 population from drug overdoses for these 22 states in the years before and after the implementation of Obamacare.

Age-Adjusted Death Rates per 100,000 Population Due to Drug Overdoses in 22 Non-Medicare Expansion States, 2010-2015

Starting again with the trend for lowest death rates attributed to drug overdoses in the non-Medicaid expansion states in the years from 2010 through 2015, we see here that the trend may be described as being somewhere between flat and slightly rising.

The same observation holds true for the states recording the highest rates of death due to drug overdoses.

However, when we look at the data for the mean and median drug overdose death rates in this grouping of states, we see a slow increase in the period from 2010 through 2013, followed by a more rapid increase in the years from 2014 through 2015 for the median data, but a slower increase in the average death rate recorded in these states during these latter two years, where the average dropped below the median value in 2015.

In our final chart, we'll use animation to more directly compare what happened between the median and avarage death rates in both groups of states. If you're reading this article on a site that republishes our RSS news feed, you may want to click through to our site to see the animation (assuming you've also enabled JavaScript on your web browser).

Mean and Median Age-Adjusted Death Rates per 100,000 Population Due to Drug Overdoses in Medicaid-Expansion and Non-Expansion States, 2010-2015

The key observation to take away from this comparison is that the increase in death rates due to drug overdoses in Obamacare's Medicaid expansion states has accelerated much faster in 2014 and 2015 than what was observed in the non-Medicaid expansion states.

At this point, we do need to point out the statistical truism that correlation is not necessarily causation. For instance, it could be that the states that were more likely for economic reasons to experience increasingly higher rates of deaths from drug overdoses perhaps influenced them to choose to join in the Affordable Care Act's expansion of their states' Medicaid programs, where they hoped to cash in on the additional funding provided for Medicaid by the ACA from the federal government.

However, the data does suggest that the practices of the Medicaid program are a significant contributing factor, where the health care provided by the U.S. government is directly responsible for the increase, where we can confirm that both federal and state-level Medicaid officials have been very specifically responding to the increases in drug overdose-related death rates in the U.S. by restricting the prescription of the opioid-based medications at the center of the nation's increase in overdose deaths.

As rates of prescription painkiller abuse remain stubbornly high, a number of states are attempting to cut off the supply at its source by making it harder for doctors to prescribe the addictive pills to Medicaid patients.

Recommendations on how to make these restrictions and requirements were detailed in a “best practices” guide from the federal Centers for Medicare and Medicaid Services....

Some states’ efforts to curtail prescribing predated CMS’ bulletin. But the advisory added new fuel to the trend. States such as New York, Rhode Island and Maine adopted new prescription size limits this year, and West Virginia will require prior authorization starting next year. In the 2016 fiscal year, 22 states either adopted or toughened their prescription size limits, and 18 did so with prior authorization.

The goal is to make physicians think twice before prescribing highly addictive opioids — a change many say is necessary, especially within the state-federal health insurance program for low-income people. After all, research indicates Medicaid beneficiaries are prescribed opioids at twice the rate of the rest of the population, and are at three to six times greater risk of an overdose.

Unfortunately, the problem of the federal government-provided health care programs in contributing to the nation's increase in drug overdose death rates is not limited to the Medicaid welfare program, where similar patterns in increased opioid addiction and drug overdose death rates are being seen among Americans who receive care from the Veterans Administration (VA) and the Indian Health Service (IHS), which are the U.S. government's single payer-style health care programs.

The expansion of "free" health care provided for by the U.S. federal government through these programs and through Obamacare's expansion of the Medicaid welfare program may very well have backfired by contributing to the nation's increase in drug overdose death rates and the decline in American life expectancy.

References

U.S. Centers for Disease Control and Prevention. National Vital Statistics System, Mortality. CDC WONDER. [Online Database]. Atlanta, GA: US Department of Health and Human Services, CDC; 2016. [Note: Deaths were classified using the International Classification of Diseases, Tenth Revision (ICD–10). Drug overdose deaths were identified using underlying cause-of-death codes X40–X44, X60–X64, X85, and Y10–Y14.]

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