Political Calculations
Unexpectedly Intriguing!
September 27, 2020

The S&P 500 (Index: SPX) has come to revolve as much around the miscellaneous pronouncements of various minions of the Federal Reserve as it does about their expectations for the fundamental future business prospects of the 500 largest publicly-traded U.S. companies.

The latest sign of how deeply dependent investors have become on those pronouncements on Tuesday, 22 February 2020. Speaking to a virtual forum of Official Monetary and Financial Institutions, Chicago Fed President Charles Evans 'accidentally' set a new expectation the Fed's future monetary policy would be less expansionary than it previously communicated it would be in announcing its new average inflation target policy.

For the dividend futures-based model we use to project the future potential levels of the S&P 500, that kind of change alters the model's amplification factor, which we think shifted from +1.0 to +1.5 as a direct consequence of Evans' statement. We've visually indicated that shift in the latest update of the alternative futures chart indicating the model's future projections.

That change also occurs as investors would seem to have shifted their forward-looking focus from 2020-Q4 toward the more distant quarter of 2021-Q1, which began last week. We think that shift can be best understood as the market starting to pay much closer attention to the 2020 election, whose outcome will have considerable impact on the future for the U.S. government's fiscal policies. We anticipate investors may switch their focus back and forth between 2020-Q4 and 2021-Q1 severval times before the end of the 2020 calendar year.

We've described Evans' rate hike statement as 'accidental' since he attempted to walk it back on the next day, though the level of the S&P 500 indicates his effort, combined with the statements of other Fed officials, was unsuccessful.

Speaking of which, there was quite a lot of noise coming from the Fed's minions in the trading week ending on 25 September 2020, mostly calling for the U.S. government to step up its fiscal stimulus efforts. There was other stuff too, but that's what stood out to us in reviewing what we consider to be market-moving headlines from the week's newstream.

Monday, 21 September 2020
Tuesday, 22 September 2020
Wednesday, 23 September 2020
Thursday, 24 September 2020
Friday, 25 September 2020

Barry Ritholtz presents the positives and negatives he found in the past week's economics and markets news over at The Big Picture.

Finally, for those looking for a primer of how the outcome of an election can alter the future expectations of investors, be sure to review the history of 2012's Great Dividend Raid, which we had the pleasure of documenting in real time as it happened!

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September 25, 2020
Like sands through the hourglass, so our the coins in our jars...

How much do you need to set aside each payday to save up for a big ticket item you will need to buy a few years from now?

Sure, you could do what a lot of people do and just pull out your credit card when it is time for you to buy that big ticket item, then spend the next several years paying for it and the interest your credit card company will charge you. But what if you would rather only pay once for what you know well in advance that you're going to be buying?

Better still, what if you set aside money every payday and earned a little bit of interest on it from that savings account at your bank? You wouldn't need to set aside quite so much, but all the money you would need would be ready when you're ready to pull the trigger on your planned purchase.

That's where our latest tool might be really helpful for you. Just enter the indicated data for your future purchase in the input fields below, and it will work out how much you will need to set aside from each of your paychecks until you have saved enough! [If you're reading this article on a site that republishes our RSS news feed, please click here to access a working version of the tool at our site.]

Big Ticket Item Price and Savings Information
Input Data Values
How much money are you looking to save?
What is the interest rate for your savings account?
Over how many years will you save before buying?
How often do you receive a paycheck?

Savings To Set Aside With Each Paycheck
Calculated Results Values
Amount to Save From Each Paycheck

For our default calculation, if you placed $126.69 out of every paycheck in your savings account and earned 0.8% interest on it, you would have $10,000 saved up after 3 years. If you change the interest rate to 0%, you'll find that you'll have reduced the amount you need to save by $1.52 per paycheck, which doesn't sound like much, but that's a $118.56 savings for you over three years.

If you can get a higher interest rate on your savings account, then the savings math may become more compelling. Alternatively, if you can find a way to get a discount on what you're looking to buy and are willing to adjust the timing of when you plan to buy, that will work in your favor too.

If you're wondering about the math behind the tool, it the same that big corporations use when they plan to set aside funds to pay dividends to their shareholding owners or to pay back money they've borrowed. They call these special purpose savings accounts "sinking funds", although we have yet to find a compelling explanation for why they're called that.

But you have to admit, they're an excellent way to ensure you have the money you will need when it is time to buy that costly, not-so-everyday item. Not to mention being easier to manage than three years worth of loose change tossed in a jar!

Image credit: Photo by Michael Longmire on Unsplash

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September 23, 2020

How has the cumulative distribution of income in the United States changed over the last six years?

Since the U.S. Census Bureau published its income distribution data for American individuals, households, and families for the 2019 calendar year on 15 September 2020, we thought it would be interesting to show how the distributions for each of these subgroups of the nation's population has changed over time. To do that, we've put together several animated charts to show the evolution of the distribution of income in the U.S. from 2014 through 2019.

If you're reading this article on a site that republishes our RSS news feed, you may want to click through to the original version of this article at our site to see the animations in action.

Here is the animated chart showing the shifting distribution of total money income for individual Americans:

Animation: Cumulative Distribution of Total Money Income for U.S. Individuals, 2014-2019

Next, let's look at the animated chart for the total money income distribution of American households:

Animation: Cumulative Distribution of Total Money Income for U.S. Households, 2014-2019

Finally, here is the animated chart showing the ongoing development of total money income for American families:

Animation: Cumulative Distribution of Total Money Income for U.S. Families, 2014-2019

The U.S. Census Bureau distinguishes families from households by recognizing that families are made up of individuals who live together that are related to each other by birth, marriage, or adoption. Households may consist of people who either are related or that are not related as a family.

Each of the animated charts show the distribution of nominal cumulative total money income for American individuals, households, or families, which have not been adjusted for inflation. For each of these groupings, the animations show the distribution of income in the U.S. shifting toward the right, with Americans' incomes rising over time. The rate of increase has accelerated considerably in the most recent years, although we should note that the data for 2019 was collected in early March 2020, before any negative impacts from the Coronavirus Recession would be observed.

The animations also show the shifting distribution of income in the U.S. resulting in a falling percentage of Americans with the lowest incomes during these years, while the percentage of Americans at the upper end of the nation's income spectrum has been rising.

If you would like to keep track of the latest trends for median household income, we estimate that vital statistic each month. Our next update will come on 1 October 2020, when we'll present the latest updates through August 2020. Until then, our estimate of median household income for July 2020 is the latest entry in the series.

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September 22, 2020

From time to time, we test drive new forecasting methods for stock prices to see how they perform.

Back in early November 2018, we presented a prediction for what would happen with the share price of General Electric (NYSE: GE) based on a relationship between its expected future dividends and its market cap. Let's quickly recap that old forecast:

Now that General Electric (NYSE: GE) has slashed its quarterly dividend by 91%, from $0.12 to $0.01 per share, which we estimate is about 50% more than what investors had already priced in to the stock, what can they expect next for the company's share price?

Based on the historic relationship that investors have set between the company's market capitalization and its aggregate forward year dividends since 12 June 2009, we would anticipate GE's share price falling to somewhere within a range of $3 to $7.

General Electric Market Capitalization versus Forward Year Aggregate Dividends at Dividend Declaration Dates from 12 June 2009 through 30 October 2018

Almost two years later, we can see how well the forecasting method we were testing worked, updating that original chart to show how history played out. The new chart catches the data up through 3 September 2020 to coincide with the date of GE's most recent dividend declaration:

General Electric Market Capitalization versus Forward Year Aggregate Dividends at Dividend Declaration Dates from 12 June 2009 through 3 September 2020

It didn't take long for GE to prove that prediction right, with its market cap dropping within the top end of our target range in a month's time.

But that outcome didn't last very long. Soon, GE's stock price rose above the range we had projected using nine years worth of historical data, staying well above it for a prolonged period of time. It took the onset of today's Coronavirus Recession to drop GE's market cap back within that target range. Even so, GE's market cap hasn't fallen below the midpoint of that range during any of that time, which means the method we used set the target too low.

Being able to connect a company's expected forward year aggregate dividends to its market capitalization to forecast its stock price could be a valuable way of determining whether its current share price presents a buying or a selling opportunity. Doing a better job in setting the target would better indicate which kind of investing opportunity might exist at any given time.

We have an idea for how to improve our result, which we'll explore more in upcoming weeks. To test it out though, we'd like to look at some other stock than GE, since we don't expect the company will be changing its dividend payouts anytime soon. If you have a candidate for us to consider, please drop us a line!


Dividend.com. General Electric Dividend Payout History. [Online Database]. Accessed 22 September 2020.

Ycharts. General Electric Market Cap. [Online Database]. Accessed 22 September 2020.

Yahoo! Finance. General Electric Company Historical Prices. [Online Database]. Accessed Accessed 22 September 2020.

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September 21, 2020

What effect does going back to school have on the spread of COVID-19 coronavirus infections?

The possible answers to that question have greatly concerned many parents and policymakers around the world. To find out the possible effect, we've turned to data from the state of Arizona where a combination of demographic data from the state's Department of Health Services and its three major public universities provides a window into seeing what that effect might be.

Arizona's universities started their Fall 2020 sessions by delivering course content online in August, but began providing either hybrid or traditional classroom instruction at their campuses in late-August or early September. Since we're mainly interested in how returning to the classroom might affect the spread of COVID-19 among the student age population, we looked at the total number of positive coronavirus tests reported for the Age 0 to 44 population across the state and just by Arizona State University (ASU), the University of Arizona (UA), and Northern Arizona University (NAU) at two points in time: 3 September 2020 and 18 September 2020.

We've presented the results of that exercise in the following chart showing the change in the number of coronavirus cases in the between these two points in time, where we find a mixed picture. Please click here to access a full-size version of the chart. [Update 26 September 2020: We identified an error in our original presentation that undercounted the number of cases in the Age 0-19 portion of the population. The original chart we presented is here, the following chart has been corrected. corrections in our analysis below are indicated with boldface font.]

Corrected - Back to School In Arizona: Change in Number of Reported COVID-19 Positive Test Results for Age 0-44 Age Group Between 3 September 2020 and 18 September 2020

Between 3 September 2020 and 18 September 2020, the total number of positive test results reported by Arizona's Department of Health Services increased by 6,568. Of this figure, 37% of the reported increase in cases originated at Arizona's three major public universities. The other 63% represents the total increase in cases reported in the state for its entire Age 0 to 44 population.

Here is the breakdown for the three public universities:

  • Arizona State University: 627 cases (10%)
  • Univerisity of Arizona: 1,554 cases (24%)
  • Northern Arizona University: 273 cases (4%)

The University of Arizona's high case count stands out because it is utilizing rapid antigen tests, which differ from the established COVID-19 tests performed at the other universities and at testing sites across the state. After long excluding results from these tests in its daily reported case counts, Arizona's DHS began including results from antigen tests in its statewide tallies on 17 September 2020. UA's antigen test results have had issues with false positives.

The reported data is limited because it is silent about where an individual with a positive COVID-19 test result may have been exposed to the SARS-CoV-2 coronavirus. For example, a student's exposure to the viral infection may have taken place in a classroom, a campus facility, a dorm, or even off campus. We should also note that the positive COVID-19 results for university students, faculty and staff members may also include individuals older than Age 44.

That's why breaking out data for a university's faculty and staff may be valuable, since the incidence of cases would come primarily through contact in classroom and on-campus facilities. Here, data from ASU indicates that students account for 99% of the reported cases, while faculty and staff account for just 1% of the new cases reported in our period of interest. Data going back to 1 August 2020 indicates 2% of all ASU's reported cases have been among faculty and staff members.

All these institutions are operating with classes set up to minimize the potential spread of coronavirus infections. The exceptionally low number of new positive test results among faculty and staff suggest those approaches are effective at protecting the health of older individuals who are much more at risk of health complications from COVID-19 than the student-age population, who make up the vast majority of infections on campus.

For all the testing the universities are doing, two of the three are reporting comparatively low test positivity rates, consistent with levels indicating the spread of coronavirus infections is manageable. Both ASU and NAU report their cases are below a 5% threshold.

By contrast, UA reports a 15.5% rate from its antigen tests, prompting the university to tell students on 14 September 2020 to "shelter-in-place" for two weeks. The action is expected to bring the spread of infections back down to manageable levels.

Meanwhile, falling rates of COVID-19 hospitalizations are continuing to be reported for the state. The Age 0-44 demographic is also the least likely to experience health complications from the viral infection requiring admission to hospitals, which may account for why these numbers have not been rising.

Perhaps the most significant factor behind the pattern we see in the incidence of COVID-19 infections at Arizona's major college campuses is whether or not its local community has already had significant numbers of cases. Arizona became a hotbed for infections during June 2020 and peaked in July 2020, with over 60% of its reported concentrated in the Phoenix metropolitan area (where ASU is located). Meanwhile, the Tucson metro area (the home of UA) had a moderate number of cases and greater Flagstaff (home to NAU) had has relatively low numbers.

As we've previously observed, COVID is very much a geographic phenomenon, tending to spread most where it hasn't previously been in great numbers, where local herd immunity hasn't developed. We suspect that dynamic lies behind the high number of cases at UA in Tucson now being recorded, and we fear NAU in Flagstaff may have a surge in cases in its future.

The patterns we've described for Arizona would seem to have direct bearing on the "going back to school" season for college students in other states. Dave Tufte describes what he's observing with a marked surge in COVID-19 cases now taking place in Utah. Looking over the state's data, we think the sharp increase in number of new coronavirus infections in the state may be tied to an initial exposure event coinciding with the late start of classes at Brigham Young University, which was then amplified and spread to students' home towns during the Labor Day holiday weekend a week later. Like Arizona's UA outbreak, it seems to be spreading in areas that haven't previously seen high levels of infections.

That still leaves us with one big question needing more information to be answered. Do high numbers of cases among college students take place in places that have already experienced high epidemic numbers? Arizona's data hints the answer is no, but the sample size of universities running in-person classes across the country is still pretty small.


We've used contemporary news reports to compile the COVID-19 data for the universities, and Arizona's DHS COVID-data dashboard for the state's overall figures for the Age 0-44 population.

Arizona Department of Health Services. COVID-19 Data Dashboard. [Online Application/Database]. Accessed 19 September 2020.

Eltohamy, Farah. University of Arizona reports new daily high for positive COVID-19 tests. AZCentral. [Online Article]. 3 September 2020.

Hansen, Piper J and Myscow, Wyatt. There are 983 positive coronavirus cases within the ASU community. State Press. [Online Article]. 3 September 2020.

Ackley, Madeline. ASU has 112 new COVID-19 cases in past 3 days, whil UA has 678. AZCentral. [Online Article]. 18 September 2020.

Northern Arizona University. Coronavirus updates and resources. [Online Article]. Accessed 18 September 2020.

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September 20, 2020

The dividend futures-based model we use to project the future for the S&P 500 (Index: SPX) presents some unique challenges from time to time.

In 2020, one of those challenges has been coping with changes in the model's amplification factor (m) which, after more than a decade of holding a virtually constant value, suddenly became a variable. Add to that a bubble in stock prices inflated by a Japanese investment bank, and we've had our hands full in keeping up with the changes that have driven stock prices.

The unwinding of the one-sided trades launched by the Japanese investment bank's "NASDAQ whale" combined with statements by Federal Reserve officials on Wednesday and Thursday in the past week however provided us with an opportunity to calibrate the model and empirically determine the amplification factor. Assuming investors are continuing to focus on 2020-Q4 in setting current day stock prices, it seems to have settled at a positive value of 1.0.

That's less than the value of 1.5 that held in the period prior to the NASDAQ whale's influence, where the reduction from this level is consistent with the Fed adopting a more expansionary monetary policy. Since nobody outside of Japan's SoftBank had visibility on its role in the summer stock price rally, we had previously attributed the runup in the S&P 500 to investors responding the Fed's signaling its increasing willingness to adopt a more 'dovish' policy. Now that the NASDAQ whale is out of the picture, so to speak, we can now better quantify the contribution of the Fed's signaled policy change to the summer rally, where it would appear to account for 25% of the change in the amplification factor.

This past week is when that signal was set more definitively, although as you'll see in the headlines we plucked from the week's major market-moving newstream, the Fed is still really shaky on what that new policy means.

Monday, 14 September 2020
Tuesday, 15 September 2020
Wednesday, 16 September 2020
Thursday, 17 September 2020
Friday, 18 September 2020

Meanwhile, Barry Ritholtz succinctly summarized each of the positives and negatives he found in the past week's economics and markets news.

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September 18, 2020

More or Less presenter Tim Harford talks about deliberately misleading statistical analysis in the following Numberphile video. If you have nine minutes, you'll find why its essential to maintain a healthy skepticism of both claims and counterclaims based on statistical analysis.

If you're anywhere but the U.S. or Canada today, Tim's newest book, How to Make the World Add Up, is now available for sale. If you're in the U.S. or Canada, you'll have to wait until February 2021 to get a copy that will carry a different title: The Data Detective: Ten Easy Rules to Make Sense of Statistics, which can be pre-ordered at Amazon today.

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September 17, 2020
Benford's Law Leading Digit Distribution

Can you trust the numbers the U.S. government reports daily for the number of confirmed COVID-19 cases? Can you trust China's or Italy's figures? How about the case counts reported by Russia or other nations?

2020 has been a bad year for many people around the world, mainly because of the coronavirus pandemic and many governments' response to it, which has almost made COVID-19 as much a political condition as a viral infection. Among the factors that make it a political condition is the apparent motives of political leaders to justify their policies in responding to the pandemic, which raises questions of whether they are honestly reporting the number of cases their nations are experiencing.

Telling whether they are or not is where Benford's Law might be used. Benford's Law describes the frequency by which leading digits appear in sets of data where exponential growth is observed, as shown in the chart above. The expected pattern that emerges in data showing exponential growth over time according to Benford's Law is strong enough that significant deviations from it can be taken as evidence that non-natural forces, such as fraud or manipulation for political purposes are at play.

Economists Christoffer Koch and Ken Okamura wondered if the data being reported by China, Italy and the United States for their respective numbers of reported cases was trustworthy and turned to Benford's Law to find out. We won't keep you in suspense, they found that the growth of each nation's daily COVID-19 case counts prior to their imposing 'lockdown' restrictions were consistent with the expectations of Benford's Law, leading them to reject the potential for the data having been manipulated to benefit the interests of their political leaders. Here's the chart illustrating their findings from their recently published report:

Koch, Okamura: Figure 2. First Digit Distribution Pre-Lockdown number of confirmed cases in Chinese Provinces, U.S. States and Italian Regions

But that's only three countries. Are there any nations whose leaders have significantly manipulated their data?

A preprint study by Anran Wei and Andre Eccle Vellwock also found no evidence of manipulation in COVID-19 case data by China, Italy and the U.S., and extends the list of countries with trustworthy data to include Brazil, India, Peru, South Africa, Colombia, Mexico, Spain, Argentina, Chile, France, Saudia Arabia, and the United Kingdom. However, when they evaluated COVID-19 case data for Russia, they found cause for concern:

Results suggest high possibility of data manipulations for Russia's data. Figure 1e illustrates the lack of Benfordness for the total confirmed cases. The pattern resembles a random distribution: if we calculate the RMSE related to a constant proability of 1/9 for all first digits, it shows that the RMSE is 20.5%, a value lower than the one related to the Benford distribution (49.2%).

Wei and Vollock also find issues with Russia's COVID-19 data for daily reported cases and deaths. Here is their chart summarizing the results for total confirmed COVID-19 cases for each of the nations whose data they reviewed:

Wei and Vellwock. Figure 1. Total confirmed cases for (a) the whole world and (b-q) selected countries. The black curve refers to Benford's Law probability.

They also found issues with Iran's daily confirmed cases and deaths, but not enough to verify the nation's figures have been manipulated.

Previously on Political Calculations


Koch, Christopher and Okamura, Ken. Benford's Law and COVID-19 Reporting. Economics Letters. Volume 196, November 2020, 209573. DOI: 10.1016/j.econlet.2020.109573.

Wei, Anran and Vellwock, Andre Eccel. Is the COVID-19 data reliable? A statistical analysis with Benford's Law. [Preprint PDF Document]. September 2020. DOI: 10.13140/RG.2.2.31321.75365.

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September 16, 2020

A little over a month ago, a wave of coronavirus infections was cresting and beginning to recede in states across the U.S.' sunbelt. One month later, that delayed first wave of infections that struck southern and western states is clearly receding.

We've put together the following three-in-one chart to show the major trends in the daily progression of COVID-19 infections in the United States from 10 March 2020 through 15 September 2020. The three charts show:

  • A tower chart indicating the daily progression of COVID-19 tests (light blue, outermost), confirmed active cases (orange), estimated recoveries or hospital discharges (light green), hospitalizations (brown), and deaths (black, innermost).
  • A line chart tracking the U.S.' daily test positivity rate, or rather, the daily percentage of positive cases among all reported test results.
  • A line chart showing the rolling 7-day averages of the number of reported positive coronavirus cases and deaths per day for the entire U.S.

Please click here to see the full-size version of this chart.

Daily Progression, Test Positivity Rate, 7-Day Averages of Number of Confirmed COVID-19 Cases and Deaths, 10 March 2020 - 15 September 2020

After six months, the progression of SARS-CoV-2 coronavirus infections in the U.S. is as much a story about geography as it is about an epidemic. While data for the U.S. as a whole looks like it has gone through multiple waves, in reality, the virus has struck different states and regions of the country at different times, with individual locations in the U.S. only experiencing a single wave of infections.

That dynamic reality becomes more clear when we drill down into the Coronavirus Tracking Project's data at the state and territory level.

State Skyline Tower Charts

The latest update to our skyline chart showing the progression of coronavirus cases, hospitalizations, and deaths in the fifty states and six territories of the United States confirms the improving situation for coronavirus cases across much of the nation.

You can see that improvement in the 'straightening' sides of the orange-shaded portion of the towers indicating the share of each state or territory's population that has returned a positive result in coronavirus testing to detect active infections. You can also see the improvement in increasing amount of 'green' shown in many of these charts, which reflects estimates the of number of recovered coronavirus cases or that more conservatively report the number of coronavirus patient discharges from hospitals (some states don't report this data).

Progression of COVID-19 in the United States by State or Territory, 10 March 2020 through 15 September 2020

States whose COVID progression towers have bases that resemble widening cones are those that are still facing growing numbers of cases. Many of the states and territories in this category also have lower populations than states that have passed through peaks in daily reported cases, which suggests future numbers of new cases will continue to fall when compared to current levels.

Meanwhile, a handful of states and territories have towers that look like needles when presented on the same horizontal and vertical scales with respect to their populations, having experienced little spread of the infection. That may not be as positive as it sounds, because it suggests they may be more at risk of future outbreaks because their residents will not have had the exposure that would lead to the level of herd immunity that states with much larger outbreaks have developed. Developing herd immunity among the portions of the population most tolerant of the infection while protecting the most vulnerable portions can work as an alternative strategy for managing the spread of viral infections for which vaccines are not yet available.

Overall, Louisiana continues to rank at the top for percent of population with confirmed active infections during the first six months of the coronavirus pandemic in the U.S., followed by Florida, Mississippi, and Arizona.

New York versus...

In the next four charts, we compare the progression of COVID-19 cases and deaths in New York against Louisiana, Florida, Mississippi, and Arizona. Why these four states? Like New York, each has recorded a peak value of 40 or more positive new coronavirus cases per 100,000 residents on one or more days during the last six months, making their experiences directly comparable in relative scale.

Here are the four charts.

New York vs Louisiana: COVID-19 Confirmed Cases and Deaths per 100,000 Residents, 7-Day Moving Averages
New York vs Florida: COVID-19 Confirmed Cases and Deaths per 100,000 Residents, 7-Day Moving Averages
New York vs Mississippi: COVID-19 Confirmed Cases and Deaths per 100,000 Residents, 7-Day Moving Averages
New York vs Arizona: COVID-19 Confirmed Cases and Deaths per 100,000 Residents, 7-Day Moving Averages

Although each of these four states has experienced similar rates of coronavirus cases as New York, each has also underrun New York's COVID-19 death tally. Unlike New York, none of these states adopted a policy of forcing nursing homes to admit coronavirus patients without testing to determine if they were still infectious. Consequently, these states have generally outperformed New York in avoiding coronavirus deaths by reducing the rate of exposure of the SARS-CoV-2 coronavirus among the most vulnerable portion of their populations: the sick and elderly.

Of these states, Louisiana stands apart because it has experienced what appears to be a second wave of coronavirus infections at the state level. However, it might be more accurate to describe its situation as the result of different portions of the state experiencing separate waves. Its 'first wave' is attributable to New Orleans' Mardi Gras celebration that drew visitors from around the country, where outbreaks were largely concentrated within the parishes making up the city's metropolitan area in the weeks following the event.

By contrast, Louisiana's 'second wave' has mostly taken place in the state's other parishes, where the incidence of cases resembles the delayed first wave effect experienced by other states.

Finally, the Wall Street Journal has recognized Arizona's successful decentralized approach to subduing its delayed wave of coronavirus cases, which will come as no surprise to our readers.

Previously on Political Calculations

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September 15, 2020

The estimated cumulative loss of GDP to the world economy since the coronavirus pandemic began in China now tops $11 trillion.

We are basing that assessment on the trend in the trailing twelve month average of the year over year change in the atmospheric concentration of carbon dioxide measured at the remote Mauna Loa Observatory. Here, we find that the pace at which CO₂ is increasing in the Earth's air has fallen from a trailing year average of 2.91 parts per million in December 2019 to a preliminary estimate of 2.58 parts per million in August 2020.

The falling trend since December 2019 can be seen in the latest update to a chart, which shows the history of this measure against a backdrop of the timing of major world economic events since January 1960.

The cumulative decline of just 0.34 parts per million of carbon dioxide in the atmosphere roughly translates to the equivalent of a $11.3 trillion reduction in the world's Gross Domestic Product. The default values in the following tool confirm the results of the math we have previously confirmed provides estimates of lost GDP in the right ballpark. 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.

Change in Atmospheric Carbon Dioxide
Input Data Values
Change in Carbon Dioxide in Atmosphere [Parts per Million]
World Population [billions]

Change in Amount of Carbon Dioxide Emitted into Atmosphere
Calculated Results Values
Carbon Dioxide Emissions [billions of Metric Tonnes]
Estimated Change in World GDP [trillions]

The cumulative GDP loss we've estimated is climbing up toward the $12 trillion global GDP loss the International Monetary Fund projected would result from the coronavirus pandemic back on 24 June 2020.

We think our near real-time estimates are running on the high side of a reasonable range of estimates for the amount of GDP the world has lost because of the coronavirus pandemic. That said, they are also the most current picture we have to quantify the economic impact of the pandemic on the Earth's economy.


National Oceanographic and Atmospheric Administration. Earth System Research Laboratory. Mauna Loa Observatory CO2 Data. [File Transfer Protocol Text File]. Updated 9 September 2020. Accessed 13 September 2020.

Cederborg, Jenny and Snöbohm, Sara. Is there a relationship between economic growth and carbon dioxide emissions? Semantic Scholar. [PDF Document]. 2016.

Previously on Political Calculations

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