Political Calculations
Unexpectedly Intriguing!
31 March 2021

The price of the typical new home sold in the United States is rising at an unprecedented rate.

That's not something that's immediately obvious if you just look at the reported median and average new home price data reported each month. The following chart shows that data from January 2000 through February 2021:

Median and Average Monthly U.S. New Home Sale Prices, January 2000 through February 2021

What this chart does is that average new home sale prices reached a new record high based on the first estimate for February 2021 (the data will be revised at least two or three times before being finalized). And at the same time, the median new home sale price in the U.S., which is representative of the typical home sold, is just a bit off its record high, which was set in December 2020.

But to understand why that's unprecedented, we need to reframe the housing sale price data with respect to the income earned by a typical American household. In the following chart, we take the trailing twelve month average of median new home sale prices and track it with respect to the trailing twelve month average of median household income for each month from December 2000 through February 2021.

U.S. Median New Home Sale Price vs Median Household Income, Annual: 1999 to 2019 | Monthly: December 2000 to February 2021

This chart focuses on the historical period that includes the rapid inflation phases of the first housing bubble and of the second, where we find today's trend for median new home sale prices are rising despite little to no change in median household incomes. If we expand the chart to include all the available historical data going back to 1967, we see there's no other period on record where that's happened.

U.S. Median New Home Sale Price vs Median Household Income, Annual: 1967 to 2019 | Monthly: December 2000 to February 2021

As you might imagine, that trend means new homes are rapidly becoming more unaffordable for the typical American household.

Ratio of Trailing Twelve Month Averages for Median New Home Sale Prices and Median Household Income, Annual: 1967 to 2019 | Monthly: December 2000 to February 2021

At present, the median sale price of a new home sold in the U.S. is 5.12 times greater than the annual income earned by the typical American household. That ratio previously peaked at 5.45 times median household income in February 2018, before the rapid growth of median household income during the Trump administration combined with slow growth in new home sale prices to reverse what had previously been a rising trend of unaffordability. The ratio bottomed at 4.88 times median household income in May 2020.

By contrast, median new home sale prices had previously peaked at 5.21 times greater than median household income in June 2006 during the first housing bubble, where rapidly falling new home sale prices during the deflation phase accounts for the reduction in relative unaffordability.

We like this final chart because it neatly visualizes what we mean when we discuss the inflation and deflation phases of the U.S.' two previous housing bubbles. As median new home sale prices have once again decoupled from their previously established positive relationship with median household incomes, their rising level now suggests the U.S. is experiencing its third.

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30 March 2021

Political Calculations' initial estimate of median household income in February 2021 is $66,004, a reduction of $35 (or 0.05%) from the initial estimate of $66,039 for January 2021. This was the second month-over-month decline for median household income in the U.S. since the coronavirus recession bottomed in August 2020.

The latest update to the chart tracking Median Household Income in the 21st Century shows the nominal (red) and inflation-adjusted (blue) trends for median household income in the United States from January 2000 through February 2021. The inflation-adjusted figures are presented in terms of constant February 2021 U.S. dollars.

Median Household Income in the 21st Century: Nominal and Real Modeled Estimates, January 2000 to February 2021

The cause of the recent decline after months of recovery can be found in the renewed lockdown measures imposed by several state and local governments in late November or early December 2020, such as in California, Illinois, New Jersey, New York, and Pennsylvania, to identify the lockdown states with the largest populations. The data for December 2020 had indicated the rebound in median household income was slowing. The data for January 2021 confirms their negative impact. The data for February 2021 shows that negative impact continued.

With the lifting of restrictions on business operations and stay-at-home orders for residents in these states, we anticipate median household income began recovering in March 2021, which we should start seeing in the next update. The lifting of state and local government-imposed lockdown measures will be the primary driver of economic growth in 2021.

Analyst's Notes

For the first time since January 2020, the U.S. Bureau of Economic Analysis (BEA) revised its monthly resident population estimates in March 2021, which affected its estimates for December 2020 (revised downward by 59,000) and January 2021 (revised downward by 164,000).

The BEA's estimates for aggregate wage and salary data underwent a more typical revision for this dataset, with slightly downward revisions affecting the estimates for the months of October 2020 (-0.2%), November 2020 (-0.2%), December 2020 (-0.6%), and January 2021 (-0.9%). The largest adjustments were made in the period where several large population states listed in the section above had imposed lockdown orders as COVID-19 cases within them surged, reducing economic activity.

References

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 (via Federal Reserve Economic Data)]. Last Updated: 26 March 2021. Accessed: 26 March 2021.

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 (via Federal Reserve Economic Data)]. Last Updated: 26 March 2021. Accessed: 26 March 2021.

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: 10 March 2021. Accessed: 10 March 2021.

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29 March 2021

The S&P 500 (Index: SPX) closed at a new record high of 3,974.54 on the trading week ending 26 March 2021. The new highs were achieved as growth looks to be strong with the lifting of state and local government lockdown restrictions, as more states open COVID vaccine eligibility to all adults.

During the week however, stock prices traded near the lower end of the redzone forecast range, as ongoing volatility prompted by rising interest rates in the bond market led to the continuation of tech stock selloffs, as bond investors acted to minimize their losses. Stock prices gained on Friday as key inflation data came in lower than expected, lowering bond yields and boosting tech stock prices.

Alternative Futures - S&P 500 - 2021Q1 - Standard Model (m=+1.5 from 22 September 2020) - Snapshot on 26 Mar 2021

Bank stocks also got a boost on Friday after the post-market close Federal Reserve announcement it would lift restrictions on dividends for most banks after its June stress test, which boosted bank stocks. All in all, it's pretty amazing how something like lifting restrictions imposed by government entities improves the expectations for growth in the future.

Other stuff also happened during the week. Here are the market moving headlines we tracked:

Monday, 22 March 2021
Tuesday, 23 March 2021
Wednesday, 24 March 2021
Thursday, 25 March 2021
Friday, 26 March 2021

Elsewhere, Barry Ritholtz's lists the positives and negatives he found in the past week's markets and economics news.

26 March 2021

A year ago, the world went crazy with the onset of the coronavirus pandemic and the concept of a lockdown.

Covid Couple at Home - Chris Greene via Unsplash

Originally pitched as a 15-day solution to "slow the spread" of SARS-CoV-2 coronavirus infections by "flattening" the epidemic curve, government-imposed lockdowns became an ongoing fact of life for many around the world. Worse, they became the go-to policy for many public officials who used them to cover their inability to adapt to the pandemic's demands, week after week after week. At this writing, parts of the world are still going into COVID lockdowns, some for the third or fourth time since the start of the pandemic.

For couples, these lockdowns has meant spending a lot more time together than would have otherwise happened in a world without the coronavirus pandemic. Having their places of work closed by lockdowns forced many couples to work from home if they could. At the same time, the lockdown stay-at-home orders prevented them from visiting others or having guests. The end result is much more "together time" than anyone would have imagined before the pandemic.

But how much more time is that? And how does that compare to a year of time couples would have spent together in the pre-COVID world?

Questions like these led BBC presenter and Cambridge doctoral maths candidate Bobby Seagull to develop a formula to quantify how much more perceived together time couples have accumulated as a result of the lockdowns.

We've taken the math and built the tool below to do it, using data collected from a survey of 2,000 couples conducted by Groupon earlier this year as the default data. Substitute your own numbers as you might like to see how your relationship has relatively aged!

If you're accessing this tool on a site that republishes our RSS news feed, please click through to our site to access a working version. We'll have more comments below the tool....

Basic Investment Information
Input Data Values
Pre-pandemic average number of hours cohabiting couples spent together per year
Boredom Factor
Hours spent together as a couple during an average week (excluding weekends) during the pandemic
Number of weeks working from home together since the pandemic began
Hours spent together as a couple on an average weekend during the pandemic
Number of weekends since the pandemic began

How Much Has Being in Lockdown Aged Your Relationship?
Calculated Results Values
Lockdown "Dog Years" (How Much Older Your Relationship Seems)

Most of the data input items are very straightforward, but one represents a subjective judgement. The "Boredom Factor" represents how the lack of options for entertainment or away-from-home social gathering contributes to making time spent with your partner seem like more time is passing than is really the case. Which is to say that often being bored 'ages' your relationship.

In the tool, we've opted to make that factor a "Yes" or "No" proposition, where if you feel you've experienced the boredom factor, selecting "Yes" will double the amount of additional time you have spent together outside of what would have been the case before being locked down.

If you select this factor, the final answer then is the 'perceived' amount of time your relationship has aged, as a multiple of the time you would have had together without the pandemic. Not selecting it will give you an estimate of the actual number of equivalent pre-pandemic years the additional time spent together you have accumulated in lockdown.

All in all, the result is a number that, for most, will be the equivalent of multiple years of time together as a couple. A result of one, on the other hand, would mean that your time in lockdown went about the same as it would have in a pre-lockdown world.

Either way, it's an interesting way to approach the question, which is why we took on the project!

Image credit: Photo by Chris Greene on Unsplash

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25 March 2021

This is an opportune time to take a snapshot of Arizona's high quality COVID-19 data. Beginning on 24 March 2021, the state opened its COVID-19 vaccination program to all Arizonans Age 16 and older. Meanwhile, the state's COVID Data Dashboard has been updated with all available data up through 23 March 2021.

That means we have a nearly ideal setup for seeing how the expansion of the state's COVID vaccination program affects the trends for positive test results, deaths, new hospital admissions, and ICU bed usage for COVID-19. The charts below represent the "before" snapshot of those trends, just before the expansion of Arizona's vaccination program. Please click on the charts to access their full-size versions.

Arizona Positive COVID-19 Test Results by Date of Sample Collection, 1 January 2020 - 23 March 2021
Arizona New COVID-19 Deaths by Date of Death Certificate, 1 January 2020 - 23 March 2021
Arizona New COVID-19 Hospitalizations by Date of Admission, 1 January 2020 - 23 March 2021
Arizona COVID-19 ICU Bed Usage, 10 April 2020 - 23 March 2021

For all charts, we see that the rates of incidence for new COVID cases, deaths, hospital admissions, and ICU bed usage have continued trending downward since our last update two weeks ago. The data is also providing an indication of the effectiveness of the COVID-19 vaccines.

That effectiveness is primarily seen in the much faster rate of decline of deaths from COVID since Arizona's vaccinations of elderly residents began in late December 2020, which began falling faster than the rate that was seen following the peak of Arizona's first wave of deaths attributed to COVID-19 earlier in 2020 in the expected lag period of 17-21 days. In the chart that earlier rate of decline is shown by the heavy black lines. We see the rate of COVID-19 deaths after their effectiveness buildup period falls much more steeply than was seen in the summer of 2020, when no vaccines existed.

That's the most pronounced change, although the data for new hospital admissions and ICU bed usage show similar, but smaller improvements in their trends. The data for positive COVID-19 test results however shows the least difference, which we would not consider significant.

We wondered why we would see that pattern, and found a potential answer in a pre-print medical paper from Public Health England (PHE). PHE finds the vaccines are highly effective in reducing severe cases of COVID-19 in older adults, which is the portion of the population most at risk of death from SARS-CoV-2 coronavirus infections.

Arizona's data would seem to be bearing that out. In reducing the severity of COVID-19 among those who have been vaccinated, the result is reduced deaths and hospitalizations, but not necessarily reduced cases. The vaccines would appear successful in increasing the survivability of those who have been vaccinated.

That brings us to our final chart, which we've taken from Arizona's COVID-19 Data Dashboard for vaccine prioritization. It shows the progress the state has made in vaccinating its high priority groups, in addition to its progress in vaccinating Arizonans by age group:

Arizona COVID-19 Vaccination Rates by Current Priority Group and by Age Group

Targeting the vaccination program to the elderly population has proven successful in improving the trends for COVID-19 in Arizona, especially for deaths.

Previously on Political Calculations

Here is our previous coverage of Arizona's experience with the coronavirus pandemic, presented in reverse chronological order.

References

We've continued following Arizona's experience during the coronavirus pandemic because the state's Department of Health Services makes detailed, high quality time series data available, which makes it easy to apply the back calculation method to identify the timing and events that caused changes in the state's COVID-19 trends. This section links that that resource and many of the others we've found useful throughout the coronavirus pandemic.

Arizona Department of Health Services. COVID-19 Data Dashboard: Vaccine Administration. [Online Database]. Accessed 24 March 2021.

Stephen A. Lauer, Kyra H. Grantz, Qifang Bi, Forrest K. Jones, Qulu Zheng, Hannah R. Meredith, Andrew S. Azman, Nicholas G. Reich, Justin Lessler. The Incubation Period of Coronavirus Disease 2019 (COVID-19) From Publicly Reported Confirmed Cases: Estimation and Application. Annals of Internal Medicine, 5 May 2020. https://doi.org/10.7326/M20-0504.

U.S. Centers for Disease Control and Prevention. COVID-19 Pandemic Planning Scenarios. [PDF Document]. Updated 10 September 2020.

More or Less: Behind the Stats. Ethnic minority deaths, climate change and lockdown. Interview with Kit Yates discussing back calculation. BBC Radio 4. [Podcast: 8:18 to 14:07]. 29 April 2020.

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24 March 2021

Today marks the anniversary of the most pivotal moment in New York Governor Cuomo's COVID nursing home deaths scandals. Because one year ago today, Governor Cuomo and senior members of his administration reached the point of panic as they struggled to address the greatest challenge of his tenure in office.

We originally presented that story on 12 May 2020. Today, we're re-running that original article, in which we recreated critical information that influenced the most consequential decision Governor Cuomo made on that day. The deadly repercussions of what resulted from that day of panic are still rippling through New York and making national news a year later. Let's get started....


COVID-19 - Martin Sanchez via Unsplash: https://unsplash.com/photos/Tzoe6VCvQYg

We're fascinated with how politicians use data and models in setting the policies they pursue, where knowing both what they knew and when they knew it can explain a lot about why they made the choices they did at the time they made them.

To that end, we've been paying attention to how Governor Andrew Cuomo has been managing the difficult task of coping with the coronavirus epidemic in New York, and in New York City in particular, which has been the focal point for both the number of cases and the spread of the SARS-CoV-2 coronavirus across the United States. We've assembled a timeline of Governor Cuomo discussing the predictive models for how fast the coronavirus infection would spread within New York, which provides insight into how that information affected his decisions for how to allocate the limited health care resources over which he had influence during the worst part of the epidemic in his state.

We're going to pick up the action shortly after 7 March 2020, the date Governor Cuomo declared a state of emergency because of the coronavirus epidemic in New York, when the number of coronavirus cases within the state had 'soared' to 89. The following article is the earliest in which we find a reference to coronavirus modeling projections for New York City, which had been put together by New York City Mayor Bill de Blasio's staff:

9 March 2020: Coronavirus Cases in New York State Rise to 105:

Mayor Bill de Blasio said Sunday that the city had 13 confirmed cases, including a new case of a man in the Bronx. Based on modeling, his team estimated there could be 100 cases in the next two or three weeks, but for most people, the illness would result in very mild symptoms.

Three days later, New York City had nearly reached that total and was set to blast through it, prompting Governor Cuomo to ban all public events with more than 500 people in attendance and to require gatherings with fewer than 500 people to cut capacity by 50%. The faster than previously projected growth in the number of COVID-19 infections drove a change in public policy.

Four days after that, Governor Cuomo had clearly been presented with projections that showed the exponential growth in the number of cases that had gotten underway in New York.

16 March 2020 - Audio & Rush Transcript: Governor Cuomo is a Guest on CNN's Cuomo Prime Time:

"I see a wave and the wave is going to break on the health care system ... You take any numerical projections on any of the models and our health care system has no capacity to deal with it."...

"Yeah. I think you look at that trajectory, just go dot, dot, dot, dot, connect the dots with a pencil. You look at that arc, we're up to about 900 cases in New York. It's doubling on a weekly basis. You draw that arc, you understand we only have 53,000 hospital beds total, 3,000 ICU beds, we go over the top very soon."

At this point, Governor Cuomo was beginning to appreciate that the thousands of hospital beds across the state of New York were really a scarce resource. He expanded on that realization the next day after an overnight surge in the number of reported cases:

17 March 2020 - Video, Audio, Photos & Rush Transcript: Governor Cuomo Announces Three-Way Agreement with Legislature on Paid Sick Leave Bill to Provide Immediate Assistance for New Yorkers Impacted By COVID-19:

"There is a curve, everyone's talked about the curve, everyone's talked about the height and the speed of the curve and flattening the curve. I've said that curve is going to turn into a wave and the wave is going to crash on the hospital system.

I've said that from day one because that's what the numbers would dictate and this is about numbers and this is about facts. This is not about prophecies or science fiction movies. We have months and moths of data as to how this virus operates. You can go back to China. That's now five, six months of experience. So just project from what you know. You don't have to guess.

We have 53,000 hospital beds in the State of New York. We have 3,000 ICU beds. Right now the hospitalization rate is running between 15 and 19 percent from our sample of the tests we take. We have 19.5 million people in the State of New York. We have spent much time with many experts projecting what the virus could actually do, going back, getting the China numbers, the South Korea numbers, the Italy numbers, looking at our rate of spread because we're trying to determine what is the apex of that curve, what is the consequence so we can match it to the capacity of the health care system. Match it to the capacity of the health care system. That is the entire exercise.

The, quote on quote, experts, and by the way there are no phenomenal experts in this area. They're all using the same data that the virus has shown over the past few months in other countries, but there are extrapolating from that data.

The expected peak is around 45 days. That can be plus or minus depending on what we do. They are expecting as many as 55,000 to 110,000 hospital beds will be needed at that point. That my friends is the problem that we have been talking about since we began this exercise. You take the 55,000 to 110,000 hospital beds and compare it to a capacity of 53,000 beds and you understand the challenge."

Faced with the potential shortage of needing 110,000 beds and only having 53,000 to provide care to coronavirus patients in New York, Governor Cuomo lobbied President Trump for support, which resulted in President Trump ordering the U.S. Navy's hospital ship USNS Comfort to sail to New York City the next day, and also lobbied for the U.S. Army's Corps of Engineers to begin identifying public facilities in New York City to be converted for use as temporary hospitals to handle the projected overflow of coronavirus patients from regular hospitals.

USNS Comfort would arrive in New York City on 30 March 2020, and the Army Corps of Engineers would have 1,000 beds ready at New York City's Javits Center ready on 27 March 2020, and were working to expand it to a 2,500 bed temporary hospital facility by 1 April 2020. But during the time in between, the updated projections of the coronavirus models led Governor Cuomo to panic.

24 March 2020: Andrew Cuomo: Apex of coronavirus outbreak in NY two or three weeks away:

Cuomo, speaking at his daily COVD-19 briefing in Manhattan, said the state's projection models now suggest the apex of the coronavirus crisis could hit New York within 14 to 21 days, rather than the 45 days the state projected late last week.

He likened it to a "bullet train" headed for New York, urging the federal government to deploy as many ventilators and as much protective medical gear it can to the state as quickly as possible.

"Where are they?" Cuomo said. "Where are the ventilators? Where are the masks? Where are the gowns? Where are they?”

At this point, we should show what one of the more influential coronavirus models that Governor Cuomo was using looked like. The following chart is taken from the Institute for Health Metrics and Evaluation (IHME)'s 25 March 2020 projections showing its estimates of the minimum, likely, and maximum number of additional hospital beds that would be needed in the state of New York to care for the model's expected surge of coronavirus patients.

IHME Forecast of All Hospital Beds Required for COVID-19 Care Beyond Available Capacity in New York State, Projection from 25 March 2020

This is just one of several coronavirus models whose projections were being combined and presented to Governor Cuomo by consultants from McKinsey & Co., where the IHME's coronavirus model's projections for New York are consistent with the figures and timing of a peak cited by Governor Cuomo in the days preceding his panic.

Faced with what appeared to be an imminent shortage of hospital beds and other medical resources, the Cuomo administration appears to have adopted an emergency triage strategy, one that would have devastatingly deadly consequences. Here, to free up as many beds as possible in New York's near-capacity hospitals, the Cuomo administration would try to move as many patients infected with the SARS-CoV-2 coronavirus as they could out of these facilities into others, even though they could still be contagious and present the risk of spreading infections within the facilities to which they would be transferred.

25 March 2020: The facilities in which they chose to place them were predominantly privately run nursing homes, where a directive issued by the state's Department of Health on 25 March 2020 mandated they must admit them into their facilities, where refusals could mean the loss of their New York state-issued licenses to operate.

New York Department Of Health Directive to Nursing Homes Mandating Admission of Coronavirus-Infected Patients, 25 March 2020

Flashing forward to the end of March 2020, the coronavirus epidemic forecast models Governor Cuomo was using in making his decisions were pointing to the peak still being ahead:

Cuomo said various predictive models being used by New York indicate the apex of the surge for hospitals will come anywhere from 7 to 21 days from now.

“The virus is more powerful, more dangerous than we expected,” Cuomo said. “We’re still going up the mountain. The main battle is on top of the mountain.”

Four days later, the coronavirus models were predicting the peak was almost upon New York:

While giving an update Saturday on the frantic work to ready New York hospitals for the most intense period of the coronavirus (COVID-19) crisis, Gov. Andrew Cuomo said that the state’s models put the so-called apex about four-to-eight days out.

“By the numbers, we’re not yet at the apex. We’re getting closer,” he said at his daily press briefing. “Depending on whose model you look at, they’ll say four, five, six, seven, days, some people go out 14 days. But our reading of the projections is that we’re somewhere in the seven-day range. Four, five, six, seven, eight-day range.”

“Part of me would like to be at the apex, and just, let’s do it,” Cuomo continued. “But there’s part of me that says it’s good that we’re not at the apex because we’re not yet ready for the apex, either. We’re not yet ready for the high point...the more time we have to improve the capacity, the better.”

But on 6 April 2020, the IHME model revised its estimates for New York and the U.S. downward, indicating the peak Governor Cuomo feared would overwhelm New York's hospitals was not going to come anywhere close to what it had previously projected. On 8 April 2020, it indicated New York had already passed its peak in number of daily new cases.

Ordinarily, that would be a good thing. Except, Governor Cuomo had taken an action by which he intended to avoid the spectacle of having pictures of sick New Yorkers not able to get medical treatment in the media, but instead ensured the state's death toll from its coronavirus epidemic would no longer be small. That part of the story has its own special timeline, which we've moved here from the bottom of the article where we had previously been piecing together this part of the story of COVID-19 in New York....

Image credit: unsplash-logoMartin Sanchez


The explosion of Cuomo scandal news has prompted us to launch a new blog to host the timeline we had been updating regularly in this space! We officially launched the new site a week ago. If you haven't yet seen it, may we introduce A Timeline of New York Governor Andrew Cuomo's Nursing Home Scandals.

The Governor Who Kills Grandmas?

Now serving all your Cuomo nursing home scandal news needs!

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23 March 2021

How has the coronavirus lockdowns affected soda consumption?

With the onset of the coronavirus pandemic in March 2020, we set aside our project evaluating the ongoing impact of Philadelphia's controversial soda tax. But now, we can tap our established data sources and use the city's beverage tax collection data to see how the consumption of taxed beverages changed in Philadelphia in response to the lockdown measures state and local politicians imposed on its residents and businesses.

The following chart illustrates Philadelphia's monthly tax revenues from its soda tax and reveals what we found in comparing the period from January 2017 through February 2020 with the coronavirus lockdown recession period of March 2020 through December 2020.

Pennsylvania imposed its first statewide coronavirus lockdown on 17 March 2020. In the "before" period, the city of Philadelphia collected an average of $6,424,887 per month from its controversial soda tax.

But from March 2020 through December 2020, the city's monthly tax revenue from the Philadelphia Beverage tax dropped by 13.3% to an average of $5,570,658 per month.

This is where we decided to have some fun with a "what if" analysis. According to Harvard's soda tax advocates, a $0.01 per ounce tax increase on beverages would increase prices of taxed beverages by 16.3%, causing soda consumption to fall by 20%. The advocates believe the resulting reduction in soda consumption provides health benefits in the form of the reduced incidence of obesity and diabetes.

The 13.3% reduction in Philadelphia's soda tax collections represents the amount by which Pennsylvania's coronavirus lockdown restrictions have reduced soda consumption in the city. Going by the Harvard researchers' study, the coronavirus lockdown recession has provided the health benefits of the equivalent of an additional $0.00665 per ounce increase in the Philadelphia Beverage Tax, reducing the incidence of both obesity and diabetes in Philadelphia.

Does anyone really believe that happened in Philadelphia during the coronavirus pandemic?

References

City of Philadelphia. Department of Revenue. City Monthly Revenue Collections. [Online Database]. Accessed 19 March 2021.

Harvard T.H. Chan School of Public Health CHOICES (CHildhood Obesity Intervention Cost-Effectiveness Study) Project. Brief: Cost-Effectiveness of a Sugar-Sweetened Beverage Excise Tax in 15 U.S. Cities [PDF Document]. 12 December 2016.

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22 March 2021

Are interest rates on long term bonds rising because of expectations of post-Coronavirus Recession growth? Or are they rising because the specter of inflation is becoming a real problem?

The as yet unknown answers to these questions is having an effect on the U.S. stock market, because bond investors have hedged their bets on bond yields over the past year by buying up high-flying tech stocks. For them, rising interest rates on bonds means losses, which they can and have offset by selling their tech-heavy stock holdings.

That dynamic helps explain why the tech-heavy Nasdaq has born the brunt of recent drops in stock prices in recent weeks, where the S&P 500 (Index: SPX) has been affected because of where the index overlaps those stocks. As you can see in the latest update to the alternative futures chart, the S&P 500 has generally risen along with the stocks that will benefit most from a post-Coronavirus Recession recovery.

Alternative Futures - S&P 500 - 2021Q1 - Standard Model (m=+1.5 from 22 September 2020) - Snapshot on 19 Mar 2021

The Federal Reserve, for its part, announced on Wednesday, 17 March 2021 that it would keep the short-term interest rates it controls at or near the zero level, indicating they are willing to allow inflation room to rise. On paper, setting that expectation should boost tech stocks, which is what happened after the Fed's meeting.

But the Fed isn't the only institution whose policies affect interest rates. On Friday, actions by the U.S. Treasury Department contributed to an environment where short term interest rates for U.S. Treasuries dropped below 0% and became negative, as President Biden's "stimulus" spending starts to get underway, dramatically increasing the amount of money the U.S. government borrows. The Fed's minions signaled they were comfortable with allowing interest rates become negative, which is a change from the expectations they had previously set.

The sudden arrival of negative short term interest rates and rising long term interest rates spells turmoil for the bond market, which will affect the stock market.

Meanwhile, other stuff happened during the week with market-moving potential. Here's our summary of the headlines for those stories:

Monday, 15 March 2021
Tuesday, 16 March 2021
Wednesday, 17 March 2021
Thursday, 18 March 2021
Friday, 19 March 2021

Did we catch all the news that mattered to the market? If you are looking for another view of the news of the week that was, check out Barry Ritholtz' list of positives and negatives he found in the past week's markets and economics news.

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