Political Calculations
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November 18, 2019

The S&P 500 (Index: SPX) marked its third consecutive week of closing at new record high values, ending Friday, 15 November 2019 at 3,120.46, with health care stocks providing the upward momentum to close out the second full week of November 2019.

Overall, the level of the S&P 500 looks like its running a bit hot compared to where our dividend futures-based model, but since it's projecting a rising trend over the next week, it could be described as simply being slightly ahead of itself.

Alternative Futures - S&P 500 - 2019Q4 - Standard Model - Snapshot on 15 Nov 2019

The level of the S&P 500 is generally consistent with investors either focusing ahead on either 2020-Q1 or 2020-Q3 in setting stock prices, although we think 2020-Q3 is the current focus because the projected future dividends expected in that quarter has been rising over the last several weeks.

Meanwhile, there's the ever present concern about what the Federal Reserve might do with setting the basic interest rate it controls. On that count, the CME Group's FedWatch Tool is projecting the investor expectation that the Fed will cut rates by a quarter point during 2020-Q4, but it wouldn't take much to shift the probabilities of such a rate change earlier by a quarter, so 2020-Q3 will very much continue to be a forward-looking focal point for investors.

CME Group FedWatch Tool Probabilities of Federal Funds Rate Changing at Future FOMC Meeting Dates, Snapshot on 15 November 2019

Meanwhile, the flow of new information offered little in the way of negativity for the U.S. stock market during the second week of November 2019. Here are the more significant headlines we flagged during the week that was:

Monday, 11 November 2019
Tuesday, 12 November 2019
Wednesday, 13 November 2019
Thursday, 14 November 2019
Friday, 15 November 2019

Barry Ritholtz succinctly summarized the positives and negatives he found in last week's economics and market-related news, where the #1 positive is that the market keeps doing the most bullish thing it can do: keep making new highs!

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November 15, 2019

Imagine if the anti-vaccine movement succeeded in their firehosing strategy for spreading their arguments against vaccination and convinced millions of people to abandon getting protection from highly contagious diseases by rejecting vaccines. Given the state of modern medicine, would they really face that big of a risk?

We don't have to spend much time wondering what would happen in such a scenario, because there is a highly contagious disease for which no vaccine exists that is currently sweeping across national borders, killing millions that have become infected by it despite the best efforts of modern medicine. That disease is called African Swine Fever and it is responsible for hundreds of millions of deaths in the populations of swine that become infected with it.

African swine fever is a virus that infects domestic pigs and wild boars. Infected animals get a high fever and internal bleeding. Over 90 percent of infected pigs die, mostly within a week.

The disease is transmitted through contact with infected blood or the carcasses of animals that have died from the disease. The virus can remain in them for months or even years. Scientists say humans cannot be infected.

The disease has devastated China's domestic swine herds and is moving outside China's borders into other nations, with South Korea the latest to be forced to cull hundreds of thousands of infected swine in an attempt to prevent the further spread of the disease to uninfected hogs. The following map from the United Nations' Food and Agriculture Organization reveals the spread of the epidemic in southeast Asia:

UN FAO Map: ASF situation in Asia (August 2018 to 3 October 2019)

Perhaps the scariest thing about the spread of African Swine Fever in China and across southeast Asia is that it has wreaked so much havoc and caused so many deaths in the very short time since it was first detected within China in August 2018.

Since there is neither a vaccine to resist the spread of African Swine Fever nor any treatment to cure it, the only way at present to break the spread of the disease once members of a herd have become infected is to cull infected herds and to quarantine or sequester their carcasses afterward to eradicate the continued risk from infection. African Swine Fever provides a very modern example of what life without vaccines would be like.

What if there was a vaccine for African Swine Fever? How would having a vaccine reduce the risk of becoming infected and dying from the condition for the surviving population of hogs?

The answer to these questions depend on how contagious African Swine Fever is and how much of the population that might otherwise be at-risk of becoming infected has been inoculated against the highly infectious disease. The following video introduces the math that captures these factors, which makes it possible to create an effective strategy for arresting the spread of contagious diseases:

There are two main studies that provide insight into what the value of R₀ is for African Swine Fever. The first puts the value of the basic reproduction number between 1.58 and 3.24 depending on the modeling method for considering the rate of spread of the disease among Uganda's farm-raised and wild populations of swine. The second comes from an analysis of an outbreak within the Russian Federation, which found values for R₀ ranging from 4.4 to 17.3, depending upon infection rate, variable incubation periods, and hog farming practices.

The high end of that latter range is just shy of the R₀ of 18 for measles, which is one of the most infectious diseases known to modern medicine.

Using these values, we can determine what percentage of the surviving hog population would need to be vaccinated to guard against the risk of uncontrolled contagion. We've built the following tool to do that math, where you can substitute whatever R₀ value you might choose for your own analysis, where our default value of 10.9 falls in the middle of the range from the second study we noted above, since China's farm conditions are more similar to those in Russia than they are to those in Uganda. If you're accessing this article on a site that republishes our RSS news feed, please click through to our site to access a working version of the tool.

Contagious Disease Data
Input Data Values
Basic Reproduction Value (R₀)

Population Inoculation Target
Calculated Results Values
Minimum Percentage of Population Needed to be Vaccinated to Minimize Spread of Infectious Disease

Our tool reveals that with an R₀ value of 10.9, at least 91% of the surviving uninfected population would need to be vaccinated to significant slow the spread of the contagious disease without having to either quarantine or cull the population to prevent its further spread.

Substituting 17.3 for the R₀ value raises that value to 94%. Although if you really cared about preventing as many hogs as you could from becoming infected, you would innoculate 100% of the population to fully get the disease under control.

Now imagine if we were talking about people. Given their track record, what have pseudoscience belief-driven anti-vaxxers done to earn trust from us in any of the things they say?

Previously on Political Calculations

Need a guide and examples you can use to debunk pseudoscience claims? Our Examples of Junk Science (EOJS) series provides a lot of insight you might find useful for dealing with all those who are the spiritual brethren of anti-vaxxers!


Barongo, Mike B.; Stahl, Karl; Bett, Bernard; Bishop, Richard P.; Fevre, Eric M.; Aliro, Tony; Okoth, Edward; Masembe, Charles; Knobel, Darry; and Ssematimba, Amos. Estimating the Basic Reproductive Number (R0) for African Swine Fever Virus (ASFV) Transmission between Pig Herds in Uganda. PLoS One. 2015; 10(5): e0125842. Published online 2015 May 4. doi: 10.1371/journal.pone.0125842.

Guinat, C.; Porphyre, T.; Gogin, A.; Dixon, L.; Pfeiffer, D.U.; and Gubbins S.
Inferring within-herd transmission parameters for African swine fever virus using mortality data from outbreaks in the Russian Federation. Transboundary and Emerging Diseases. Volume 65, Issue 2, April 2018. Pages e264-e271. doi: 10.1111/tbed.12748.

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November 14, 2019

Nearly at the midpoint of the quarter, Political Calculations' near real-time sampling of dividend declarations from data published by Seeking Alpha and the Wall Street Journal indicates that 2019-Q4 is looking very much like 2018-Q4, just under in the count of dividend cuts announced in the quarter to date at this point.

Cumulative Total of Dividend Cuts in U.S. by Day of Quarter, 2018-Q4 vs 2019-Q4 (QTD), Snapshot on 13 November 2019

44 calendar days into the fourth quarter of 2019, we've counted 29 dividend cut announcements, which compares with 27 in 2019-Q4, 35 in 2019-Q2, and 20 in 2019-Q3.

Breaking the dividend cut data down by type of payer, we find 18 firms that pay variable dividends make the list, predominantly from the oil and gas sector of the economy.

Overall, fourteen of these firms are in the oil and gas sector, while the remaining four represent the mining, chemical, finance, and manufacturing industries.

Meanwhile, there have been 10 firms that set their dividends independently of their revenues and earnings act to cut their dividends through this point of 2019-Q4.

Unlike the variable dividend payers, there are only two firms from the oil and gas sector. The financial sector, in which we include real estate industry-related firms, accounts for four of this category of dividend payers, while the services, consumer goods, mining, and media industries each have one representative.

All in all, we find the total number of dividend cuts so far in 2019-Q4 indicates the overall U.S. economy is relatively healthy, although the oil and gas industry and the manufacturing industries bear watching, the latter due primarily to the sustained global downturn in the automotive industry, and also the prolonged period for which Boeing has been unable to deliver its 737 MAX aircraft to its airline customers.


November 13, 2019

As part of its biennial Point-In-Time count of its homeless residents, the city of San Francisco asks roughly one eighth of its homeless population what factors were the primary cause of their homelessness. The city's 2019 report provides their responses to that question from its point in time counts from 2015, 2017, and 2019, which we've visualized in the following chart.

Primary Cause of Homelessness in San Francisco, 2015, 2017, 2019

From January 2015 through January 2019, the loss of a job represents the top response, with approximately a quarter of all surveyed homeless residents indicating that single response [1].

During these years, San Francisco's unemployment rate has fallen from 4.1% in January 2015 to 3.4% in January 2017 to 2.6% in January 2019. By itself, the city's falling unemployment rate suggests that job loss should be declining as the primary cause of homelessness because employers would be increasingly reluctant to either fire or lay off employees in such a tightening job market. Logically, since job loss is the number one cause of homelessness identified by San Francisco's homeless residents for their condition, the number of homeless in the city should also be falling.

But it's not. Instead of falling with the city's declining rate of unemployment, homelessness in San Francisco has been rising. San Francisco's 2019 Point In Time homeless count report indicates that homelessness in the city rose from 6,775 in January 2015 to 6,858 in January 2017 to 8,011 in January 2019.

It would be helpful to find out more about what kinds of jobs San Francisco's homeless residents held before they lost them, leading to their becoming homeless. Unfortunately, the survey doesn't provide that specific kind of insight, but it does provide information about the incomes earned by the portion of the city's homeless residents who are employed, who account for about 12% of the surveyed homeless population.

Assuming the jobs of the working homeless provide similar levels of income as the jobs that many homeless San Franciscans held before they lost them and became homeless, this data may tell us about their earning potential. In the following chart, we've constructed the cumulative distribution of income for San Francisco's employed homeless residents, where we find that roughly 85% earn far below the annual income that might be earned by working full time at the city's statutory minimum wage.

Cumulative Distribution of Income from Employment Earned by San Francisco Homeless, 2015, 2017, 2019

The city has been steadily increasing its statutory minimum wage rates, which in January 2015 stood at $11.05 per hour. In January 2017, the city's minimum wage was $13.00 per hour, and in January 2019, was $15.00 per hour [2].

With a falling unemployment rate and a rising minimum wage, we should see the cumulative distribution of income earned by working homeless San Franciscans shift to the right in each year. But we only see that from 2015 to 2017 in the chart above, and only for equivalent annual incomes between $1,200 and $18,000, where we find no meaningful shift for incomes above that level, nor do we see any significant change over all incomes from 2017 to 2019.

Since San Francisco imposes a statutory minimum wage, we can estimate how many hours the city's employed homeless are working at their jobs. The following chart maintains the cumulative distribution of income on the vertical axis, replacing the annual incomes in the horizontal axis with the equivalent hours worked at the city's mandated minimum wage.

Cumulative Distribution of Estimated Hours Worked at Minimum Wage Earned by San Francisco Homeless, 2015, 2017, 2019

This chart is a little more telling. Even at minimum wage, we find that over 85% of the city's employed homeless work less than full time year round, which we define as 40 hours per week, 52 weeks per year, or 2,080 hours per year. Only working part time at minimum wages would severely limit their ability to earn incomes sufficient to avoid being homeless [3].

Below the median 50% mark, we find that hours worked increased for this portion of the working homeless from 2015 to 2017, as unemployment fell and minimum wages rose. But from 2017 to 2019, as unemployment continued to fall and the minimum wage continued to rise, their hours worked fell back to 2015's levels.

Above the median 50% mark, we see hours worked decline from year to year, even though the city's unemployment rate falls and as the city's minimum wage rises. Combined with the income distribution data, this pattern suggests that the rising minimum wage either enables the homeless persons to choose to work less while earning similar levels of income or that their employers are unable to provide as many hours for them to work at the higher minimum wage, limiting any benefit they might obtain from an increased minimum wage.

It would be really interesting to analyze a more detailed breakdown of the earned income data for San Francisco's homeless as well as more information about their employers and employment.


[1] The survey allows for multiple responses to be recorded for the question. The report lists only the top responses given by the surveyed population.

[2] San Francisco's biennial point-in-time counts of its homeless population took place during January 2015, January 2017, and January 2019. The indicated minimum wages are those that applied in these months.

[3] Among the surveyed population, only 1-2 individuals per year who were counted as homeless earned annual incomes that would place them above the threshold that coincides with working full time, year round at the city's statutory minimum wage rates. That's makes for quite a lot of income inequality among San Francisco's homeless!

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November 12, 2019

The epidemic of African Swine Fever within China is estimated to have "wiped out a third of China's pig population", or about 15% of the world's domestic hog population.

The loss of supply has resulted in a meteoric rise of pork prices in China, which in turn, has compelled China's leaders to seek relief in the global market.

China is scouring the world for meat to replace the millions of pigs killed by African swine fever (ASF), boosting prices, business and profits for European and South American meatpackers as it re-shapes global markets for pork, beef and chicken.

The European Union, the world’s second largest pork producer after China, has ramped up sales to the Asian giant although it can only fill part of the shortfall caused by ASF. Argentina and Brazil have approved new export plants to meet demand and are selling beef and chickens, as well as pork, to fill the gap. U.S. producers, however, have been hampered due to tariffs imposed by Beijing.

But China's leaders have since relaxed the country's tariffs on U.S.-produced pork, reducing them from their previous relatiatory level of 72% down to a pre-tariff war level of 12% on 13 September 2019. Since that announcement, the country's pork imports have surged:

A hunger for imported pork is one reason underlying Beijing’s decision to exempt US pork from additional tariffs, which in turn helped to de-escalate trade tensions and paved the way for resumption of trade talks.

Chinese imports of U.S.-produced pork is tracked by the U.S. Census Bureau under the category of "meat of swine". The following chart shows the annual value of U.S. pork exports to both the world and to China from 1992 through 2018, and the year-to-date value of exports for the first nine months of 2019, where although 2019 is missing data from the final quarter of the year, we can already confirm that China's share of U.S. exports to the world is reaching new heights.

Value of U.S. Pork Exports, and China's Percent of World Total, 1992-2019 (YTD)

Official reports from China suggest that this increase in pork exports to China may be short-lived, with China's pig herd projected to recover to pre-African Swine Fever levels in 2020.

That projection may be optimistic however because it likely underestimates the full recovery process, where in order to increase China's hog herds, domestic producers will need to retain more hogs to sire the next generations, reducing the number that will be able to be processed for Chinese pork consumption. It's technically possible the size of the herd may recover in 2020, but the amount of meat that reaches China's consumers will remain in shortage, where Chinese demand to import pork will remain elevated for longer.

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November 11, 2019

The S&P 500 (Index: SPX) continued setting new highs in the first full week of November 2019, buoyed by positive changes in the outlook for dividends and earnings for the component companies that make up the index, which ended the week at an all-time-high closing value of 3,093.08.

We're nearing the end of the period to which our impromptu redzone forecast applies, where the trajectory of the S&P 500 appears to be converging with the expectations associated with the alternate trajectory for 2020-Q3, indicating that investors are focusing their attention on that distant future quarter.

Alternative Futures - S&P 500 - 2019Q4 - Standard Model - Snapshot on 8 Nov 2019

That makes sense because this future quarter has seen the greatest change in the outlook for its dividends during the last three weeks. Meanwhile, the prospects for the Fed taking action in this quarter to cut the Federal Funds Rate dropped back below the 50% mark in the last week, with the latest probabilities of rate changes at various upcoming Fed meeting dates from the CME Group's FedWatch Tool suggesting that investors are leaning against any rate changes in 2020.

CME Group FedWatch Tool Probabilities of Federal Funds Rate Changing at Future FOMC Meeting Dates, Snapshot on 08 November 2019

Investor expectations have changed by quite a wide margin during the past several weeks. Here is some of the new information that investors absorbed into their expectations from the past week:

Monday, 4 November 2019
Tuesday, 5 November 2019
Wednesday, 6 November 2019
Thursday, 7 November 2019
Friday, 8 November 2019

Over at The Big Picture, Barry Ritholtz outlines the positives and negatives he found hidden among the past week's economics and market-related news.

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November 8, 2019

When it comes to the world of fashion, maths are often relegated to a behind-the-scenes role. Designers use math to transform fabric into three-dimensional garments to fit anyone from say size 2 to size 3X while making the design affordable for consumers. The bottom line is there wouldn't be an apparel industry if fashionistas didn't also pay attention to the retail math needed to keep a business afloat.

Some designers also use math as inspiration in their designs. Senegal's Diarra Bousso incorporates a considerable amount of maths into her designs for her Diarrablu label. Here's a short video introduction to her work:

In the video, you can see how she applies combinatorics to develop a wide range of potential combinations and permutations from a limited number of pieces to appeal to consumers for her swimsuit line, a philosophy she extends to Diarrablu's convertible jumpsuit, which can be worn in 19 different ways to create a uniquely personal fashion statement from a single garment.

But the fashionable maths don't stop there. She also directly utilizes math equations to develop her print designs:

The main print for Diarrablu’s SS19 collection, titled “Ndar”, was obtained from the graphing of various equations (linear, quadratic and absolute value) to recreate randomized shapes. The shapes were then filled with colors and the patterns were cut into various shapes and went through geometric transformations such as dilations, rotations and reflections in order to create a final motif, printed on crepe and chiffon fabrics. The main equations are parabolic of the form y = ax² + bx + c.

Here's a sample of one of her print templates, where we again see the influence of combinatorics for enabling the variety of color patterns that might be developed from an otherwise simple sketch:

Diarrablu print template

Would you ever have expected something you might have had to sketch in a high school math class could become the foundation for something you could buy in a global fashion label's flagship store?

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November 7, 2019

Two months ago, we featured the first look at what dividend futures were forecasting for the S&P 500 (Index: SPX) in each quarter of 2020.

Two months later, we're updating that outlook because investor expectations for the future have changed rather dramatically, which we've presented in the animated chart. If you're accessing this article on a site that republishes our RSS news feed and you don't see the chart change every 3-4 seconds, please click through to our site to see the animation below.

Animation: Past and Projected Quarterly Dividends Per Share Futures for S&P 500, 2018-Q1 Through 2020-Q4, Snapshots on 10 September 2019 and 6 November 2019

Most of that change has taken place during the last three weeks, since 21 October 2019. The change coincides with the bulk of S&P 500 firm earnings reports, which have been generally positive and which have helped power the S&P 500 to new record highs.

In the chart above, the "past" quarterly dividend futures is the last value we recorded for the CME Group's quarterly dividends per share futures for the S&P 500 before their related contracts expired on the third Friday of the month ending the indicated quarters. The "projected" quarterly dividend futures are what the CME Group's site reports for the S&P 500 as of the close of trading on 6 November 2019.

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November 6, 2019

The carnage from the U.S-China tariff war continued through September 2019, where trade data for the month just released by the U.S. Census Bureau indicates year-over-year declines from September 2018's tariff war-reduced figures.

The following chart captures that observation as measured by the year-over-year growth rate of the exchange-rate adjusted value of trade between the U.S. and China, where both China and the U.S. fall well within negative growth territory.

Year Over Year Growth Rate of U.S.-China Trade, January 1986 - September 2019

Looking next at the combined value of the goods and services the U.S. and China traded with each other, we find the gap between the pre-trade war trend and the trailing twelve month average of the value of goods exchanged between the U.S. and China expanded to $12.8 billion in September 2019. Since March 2018, when the tariff war began, the cumulative loss in the total value of trade between the two nations has grown to $86.9 billion.

Combined Value of U.S. Exports to China and Imports from China, January 2008 - September 2019

Both the U.S. and China are being negatively impacted by the tariff war.


U.S. Census Bureau. Trade in Goods with China. Accessed 5 November 2019.

Board of Governors of the Federal Reserve System. China / U.S. Foreign Exchange Rate. G.5 Foreign Exchange Rates. Accessed 5 November 2019.


November 5, 2019

October 2019 presents a mixed picture for the relative health of dividend paying companies in the U.S. stock market. The good news is that the number of dividend cuts, a measure of the relative level of distress in the U.S. economy, dropped below the level that suggests the U.S. economy is experiencing recessionary conditions. The bad news is that the number of dividend increases recorded during the month was at its lowest level for any October since 2011, where this measure indicates that growth in the U.S. economy is slowing down.

The following chart shows the number of U.S. firms either increasing (blue) or decreasing (red) their dividends in each month since January 2004.

Number of Public U.S. Firms Increasing or Decreasing Their Dividends Each Month, January 2004 thropugh October 2019

Before we review the metadata for October 2019's dividend payers, we need to catch up with Standard & Poor's revisions to its dividend statistics for the previous month. Here is how the numbers for September 2019 changed from what S&P previously reported in early October 2019.

  • Dividend declarations for September 2019 was revised up from 3,686 to 4,020.
  • Extra (or special) dividend payments were revised upward from 21 to 24.
  • Dividend rises changed from 50 to 67 for the month.
  • Resumptions were revised upward from 7 to 8.
  • The number of dividend cuts remained unchanged at 27.
  • Omitted dividends were revised up from 7 to 8 for September 2019.

With all the figures either increasing or unchanged, we suspect that S&P's automated systems simply missed a portion of September 2019's dividend declarations when the data was originally reported, where revisions like these are rare events. Having caught those numbers up now, let's turn our attention to October 2019's dividend metadata and how it changed from the revised September 2019 figures and also how it changed from the October of a year earlier.

  • A total of 3,192 U.S. firms declared dividends in October 2019, a decrease of 828 from the 4,020 recorded in September 2019. That figure is also 462 lower than what was recorded a year ago in October 2018.
  • 41 U.S. firms announced they would pay a special (or extra) dividend to their shareholders in October 2019, an increase of 17 over the number recorded in September 2019 and an increase of 3 over the number recorded a year ago in October 2018.
  • 152 U.S. firms announced they would boost cash dividend payments to shareholders in October 2019, an increase of 85 over the number recorded in September 2019, and a decrease of 20 from the 172 dividend rises declared back in October 2018.
  • A total of 17 publicly traded companies cut their dividends in October 2019, a decline of 12 from the 29 recorded in September 2019 and also an increase of 4 over the 13 recorded in October 2018.
  • 2 U.S. firms omitted paying their dividends in October 2019, a decrease of 6 from the number recorded in September 2019. That figure is also the same as the total recorded in October 2018.

That's what the data for dividends looks like when we look backwards toward the past, assuming S&P's data for October 2019 is complete. Later this week, we'll show you how the future expectations for the dividends of S&P 500 firms have changed since early September 2019!


Standard and Poor. S&P Market Attributes Web File. [Excel Spreadsheet]. 1 November 2019.


November 4, 2019

The S&P 500 (Index: SPX) ended the final trading week of October 2019 on a very high note, setting a new record high closing value of 3,066.91 on Friday, 1 November 2019.

The week carried quite a lot of positive news, including much better than expected U.S. jobs numbers, a third quarter point rate cut from the Federal Reserve, and reports the U.S. and China are getting closer to sealing a 'Phase 1' trade deal. All of that was enough to keep the trajectory of the S&P 500 tracking along at the upper edge of the redzone forecast indicated on our alternate futures spaghetti forecast chart:

Alternative Futures - S&P 500 - 2019Q4 - Standard Model with Redzone Forecast Between 8 October 2019 and 8 November 2019 - Snapshot on 1 Nov 2019

By far, the biggest news of the week was the Fed's third rate cut, which largely closed the door on the likelihood of any additional rate cuts in the remainder of 2019. The other positive economic data that came out during the week was such that investors appear to have pushed back their expected timing of the Fed's next change in the Federal Funds Rate, which the CME Group's FedWatch Tool now projects will most likely involve a quarter point rate cut in the third quarter of 2020:

CME Group FedWatch Tool Probabilities of Federal Funds Rate Changing at Future FOMC Meeting Dates, Snapshot on 01 November 2019

Given the relatively close correspondence of our redzone forecast range with the projected trajectory associated with investors focusing upon this distant future quarter in our alternate futures chart, we've been paying close attention to the news stream over the last several weeks for any evidence investors might shift their attention to this particular point of time in the future. The probabilities indicated by the FedWatch tool above are the first confirmation that this quarter is now in the mix for shaping investor expectations of the future, with changes in the amount of dividends expected to be paid out in that distant future quarter taking place shortly afterward. Those changes have boosted the trajectory associated with investors focusing primarily on 2020-Q3 to nearly coincide with the trajectory associated with 2020-Q1 in the S&P 500 alternate futures chart during this past week.

But that's not all the new information investors encountered during the week that was. Here are the headlines we flagged for their potential market moving potential during the past week.

Monday, 28 October 2019
Tuesday, 29 October 2019
Wednesday, 30 October 2019
Thursday, 31 October 2019
Friday, 1 November 2019

Elsewhere, Barry Ritholtz listed seven positives and seven negatives in the past week's economics and market-related news, in his weekly exercise aimed at maintaining an objective assessment of market-driving news events.

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November 1, 2019

Median household income in the United States reached a new record high of $66,214 in September 2019, a 0.4% increase from Sentier Research's August 2019 estimate of $65,976.

The following chart shows the nominal (red) and inflation-adjusted (blue) trends for median household income in the United States from January 2000 through June 2019. The inflation-adjusted figures are presented in terms of constant September 2019 U.S. dollars.

Median Household Income in the 21st Century: Nominal and Real Estimates, January 2000 to September 2019

The year-over-year growth rate for nominal median household income in the U.S. dipped slightly from August 2019's level of 5.3% to 5.1% in September 2019, which you can see as the red line in the following chart. After adjusting for inflation, that represents a change of just 0.1%, from 3.4% in August 2019 to 3.3% in September 2019, which is shown as the blue line in the following chart.

Median Household Income in the 21st Century: Year Over Year Growth Rate, January 2001 to September 2019

Analyst's Notes

The alternate methodology we developed for estimating median household income puts its level at $65,607 in September 2019, which is within 1% of Sentier Research's estimate for the month.

The continuing growth of median household income in the United States since January 2018 has been something to behold, where we would have to go back to January 2008 to see the next-highest inflation adjusted value recorded before January 2018. The first 18 years of the 21st Century represent quite a period of stagnation for the growth of median household income in the United States, particularly during this 10 year period!


In generating inflation-adjusted portion of the Median Household Income in the 21st Century chart and the corresponding year-over-year growth rate chart above, we've used the Consumer Price Index for All Urban Consumers (CPI-U) to adjust the nominal median household income estimates for inflation, so that they are expressed in terms of the U.S. dollars for the month for which we're reporting the newest income data. Our data sources and other references are provided in the following list.

Sentier Research. Household Income Trends: January 2000 through September 2019. [Excel Spreadsheet with Nominal Median Household Incomes for January 2000 through January 2013 courtesy of Doug Short]. [PDF Document]. Accessed 30 October 2019. [Note: We've converted all data to be in terms of current (nominal) U.S. dollars.]

U.S. Bureau of Economic Analysis. Table 2.6. Personal Income and Its Disposition, Monthly, Personal Income and Outlays, Not Seasonally Adjusted, Monthly, Middle of Month. Population. [Online Database]. Last Updated: 31 October 2019. Accessed: 31 October 2019.

U.S. Bureau of Economic Analysis. Table 2.6. Personal Income and Its Disposition, Monthly, Personal Income and Outlays, Not Seasonally Adjusted, Monthly, Middle of Month. Compensation of Employees, Received: Wage and Salary Disbursements. [Online Database]. Last Updated: 31 October 2019. Accessed: 31 October 2019.

U.S. Department of Labor Bureau of Labor Statistics. Consumer Price Index, All Urban Consumers - (CPI-U), U.S. City Average, All Items, 1982-84=100. [Online Database (via Federal Reserve Economic Data)]. Last Updated: 110 October 2019. Accessed: 10 October 2019.


About Political Calculations

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